Overview

Brought to you by YData

Dataset statistics

Number of variables79
Number of observations90000
Missing cells3039665
Missing cells (%)42.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory279.5 MiB
Average record size in memory3.2 KiB

Variable types

Categorical24
Text16
Unsupported16
Numeric15
Boolean4
DateTime2
URL2

Alerts

site_id has constant value "MLA" Constant
listing_source has constant value "" Constant
international_delivery_mode has constant value "none" Constant
pictures_quality has constant value "" Constant
0 is highly overall correlated with condition and 4 other fieldsHigh correlation
accepts_mercadopago is highly overall correlated with attributes_attribute_group_id and 14 other fieldsHigh correlation
attributes_attribute_group_id is highly overall correlated with accepts_mercadopago and 8 other fieldsHigh correlation
attributes_attribute_group_name is highly overall correlated with accepts_mercadopago and 8 other fieldsHigh correlation
attributes_id is highly overall correlated with accepts_mercadopago and 13 other fieldsHigh correlation
attributes_name is highly overall correlated with accepts_mercadopago and 13 other fieldsHigh correlation
automatic_relist is highly overall correlated with deal_ids_0 and 1 other fieldsHigh correlation
available_quantity is highly overall correlated with catalog_product_id and 3 other fieldsHigh correlation
base_price is highly overall correlated with catalog_product_id and 5 other fieldsHigh correlation
buying_mode is highly overall correlated with accepts_mercadopago and 14 other fieldsHigh correlation
catalog_product_id is highly overall correlated with accepts_mercadopago and 16 other fieldsHigh correlation
condition is highly overall correlated with 0 and 4 other fieldsHigh correlation
currency_id is highly overall correlated with attributes_id and 14 other fieldsHigh correlation
deal_ids_0 is highly overall correlated with 0 and 21 other fieldsHigh correlation
initial_quantity is highly overall correlated with available_quantity and 3 other fieldsHigh correlation
listing_type_id is highly overall correlated with 0 and 3 other fieldsHigh correlation
non_mercado_pago_payment_methods_description is highly overall correlated with catalog_product_id and 7 other fieldsHigh correlation
non_mercado_pago_payment_methods_id is highly overall correlated with catalog_product_id and 7 other fieldsHigh correlation
non_mercado_pago_payment_methods_type is highly overall correlated with catalog_product_id and 9 other fieldsHigh correlation
official_store_id is highly overall correlated with accepts_mercadopago and 6 other fieldsHigh correlation
original_price is highly overall correlated with 0 and 11 other fieldsHigh correlation
price is highly overall correlated with base_price and 5 other fieldsHigh correlation
seller_address_country.name is highly overall correlated with attributes_attribute_group_id and 17 other fieldsHigh correlation
seller_address_state.name is highly overall correlated with seller_address_country.nameHigh correlation
shipping_free_shipping is highly overall correlated with catalog_product_idHigh correlation
shipping_local_pick_up is highly overall correlated with attributes_id and 4 other fieldsHigh correlation
shipping_mode is highly overall correlated with catalog_product_idHigh correlation
sold_quantity is highly overall correlated with attributes_attribute_group_id and 7 other fieldsHigh correlation
start_time is highly overall correlated with stop_time and 2 other fieldsHigh correlation
status is highly overall correlated with sub_status_0High correlation
stop_time is highly overall correlated with deal_ids_0 and 7 other fieldsHigh correlation
sub_status_0 is highly overall correlated with available_quantity and 15 other fieldsHigh correlation
tags_0 is highly overall correlated with accepts_mercadopago and 7 other fieldsHigh correlation
tags_1 is highly overall correlated with 0 and 22 other fieldsHigh correlation
variations_available_quantity is highly overall correlated with accepts_mercadopago and 8 other fieldsHigh correlation
variations_id is highly overall correlated with accepts_mercadopago and 6 other fieldsHigh correlation
variations_price is highly overall correlated with accepts_mercadopago and 17 other fieldsHigh correlation
variations_sold_quantity is highly overall correlated with accepts_mercadopago and 7 other fieldsHigh correlation
seller_address_country.name is highly imbalanced (> 99.9%) Imbalance
seller_address_state.name is highly imbalanced (68.5%) Imbalance
sub_status_0 is highly imbalanced (88.9%) Imbalance
shipping_free_shipping is highly imbalanced (80.6%) Imbalance
non_mercado_pago_payment_methods_description is highly imbalanced (86.8%) Imbalance
non_mercado_pago_payment_methods_id is highly imbalanced (86.8%) Imbalance
non_mercado_pago_payment_methods_type is highly imbalanced (91.3%) Imbalance
attributes_name is highly imbalanced (54.9%) Imbalance
attributes_attribute_group_name is highly imbalanced (50.9%) Imbalance
attributes_id is highly imbalanced (58.1%) Imbalance
buying_mode is highly imbalanced (86.2%) Imbalance
tags_0 is highly imbalanced (87.8%) Imbalance
accepts_mercadopago is highly imbalanced (84.7%) Imbalance
currency_id is highly imbalanced (95.0%) Imbalance
automatic_relist is highly imbalanced (72.7%) Imbalance
status is highly imbalanced (87.1%) Imbalance
warranty has 54757 (60.8%) missing values Missing
sub_status has 90000 (100.0%) missing values Missing
sub_status_0 has 89109 (99.0%) missing values Missing
deal_ids has 90000 (100.0%) missing values Missing
deal_ids_0 has 89783 (99.8%) missing values Missing
shipping_methods has 2690 (3.0%) missing values Missing
shipping_dimensions has 89978 (> 99.9%) missing values Missing
shipping_free_methods has 87311 (97.0%) missing values Missing
non_mercado_pago_payment_methods_description has 27531 (30.6%) missing values Missing
non_mercado_pago_payment_methods_id has 27531 (30.6%) missing values Missing
non_mercado_pago_payment_methods_type has 27531 (30.6%) missing values Missing
non_mercado_pago_payment_methods has 90000 (100.0%) missing values Missing
variations has 90000 (100.0%) missing values Missing
variations_attribute_combinations has 82626 (91.8%) missing values Missing
variations_seller_custom_field has 89869 (99.9%) missing values Missing
variations_picture_ids has 82626 (91.8%) missing values Missing
variations_sold_quantity has 82626 (91.8%) missing values Missing
variations_available_quantity has 82626 (91.8%) missing values Missing
variations_id has 82626 (91.8%) missing values Missing
variations_price has 82626 (91.8%) missing values Missing
attributes has 90000 (100.0%) missing values Missing
attributes_value_id has 78850 (87.6%) missing values Missing
attributes_attribute_group_id has 78850 (87.6%) missing values Missing
attributes_name has 78850 (87.6%) missing values Missing
attributes_value_name has 78850 (87.6%) missing values Missing
attributes_attribute_group_name has 78850 (87.6%) missing values Missing
attributes_id has 78850 (87.6%) missing values Missing
tags_0 has 22412 (24.9%) missing values Missing
tags has 90000 (100.0%) missing values Missing
tags_1 has 88540 (98.4%) missing values Missing
parent_item_id has 20690 (23.0%) missing values Missing
coverage_areas has 90000 (100.0%) missing values Missing
descriptions_0 has 2417 (2.7%) missing values Missing
descriptions has 90000 (100.0%) missing values Missing
pictures has 90000 (100.0%) missing values Missing
official_store_id has 89255 (99.2%) missing values Missing
differential_pricing has 90000 (100.0%) missing values Missing
original_price has 89870 (99.9%) missing values Missing
video_id has 87324 (97.0%) missing values Missing
catalog_product_id has 89993 (> 99.9%) missing values Missing
subtitle has 90000 (100.0%) missing values Missing
base_price is highly skewed (γ1 = 202.9925746) Skewed
variations_sold_quantity is highly skewed (γ1 = 32.39484032) Skewed
variations_available_quantity is highly skewed (γ1 = 57.06329737) Skewed
variations_price is highly skewed (γ1 = 84.6814162) Skewed
price is highly skewed (γ1 = 202.9925746) Skewed
stop_time is highly skewed (γ1 = 66.19226855) Skewed
initial_quantity is highly skewed (γ1 = 21.85686236) Skewed
sold_quantity is highly skewed (γ1 = 88.25444912) Skewed
available_quantity is highly skewed (γ1 = 21.87204198) Skewed
sub_status is an unsupported type, check if it needs cleaning or further analysis Unsupported
deal_ids is an unsupported type, check if it needs cleaning or further analysis Unsupported
shipping_methods is an unsupported type, check if it needs cleaning or further analysis Unsupported
shipping_tags is an unsupported type, check if it needs cleaning or further analysis Unsupported
shipping_free_methods is an unsupported type, check if it needs cleaning or further analysis Unsupported
non_mercado_pago_payment_methods is an unsupported type, check if it needs cleaning or further analysis Unsupported
variations is an unsupported type, check if it needs cleaning or further analysis Unsupported
variations_attribute_combinations is an unsupported type, check if it needs cleaning or further analysis Unsupported
variations_picture_ids is an unsupported type, check if it needs cleaning or further analysis Unsupported
attributes is an unsupported type, check if it needs cleaning or further analysis Unsupported
tags is an unsupported type, check if it needs cleaning or further analysis Unsupported
coverage_areas is an unsupported type, check if it needs cleaning or further analysis Unsupported
descriptions is an unsupported type, check if it needs cleaning or further analysis Unsupported
pictures is an unsupported type, check if it needs cleaning or further analysis Unsupported
differential_pricing is an unsupported type, check if it needs cleaning or further analysis Unsupported
subtitle is an unsupported type, check if it needs cleaning or further analysis Unsupported
variations_sold_quantity has 6966 (7.7%) zeros Zeros
sold_quantity has 74834 (83.1%) zeros Zeros

Reproduction

Analysis started2025-02-21 23:07:25.062469
Analysis finished2025-02-21 23:10:03.056562
Duration2 minutes and 37.99 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

seller_address_country.name
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
Argentina
89999 
 
1

Length

Max length9
Median length9
Mean length8.9999
Min length0

Characters and Unicode

Total characters809991
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowArgentina
2nd rowArgentina
3rd rowArgentina
4th rowArgentina
5th rowArgentina

Common Values

ValueCountFrequency (%)
Argentina 89999
> 99.9%
1
 
< 0.1%

Length

2025-02-21T23:10:03.170515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:03.290892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
argentina 89999
100.0%

Most occurring characters

ValueCountFrequency (%)
n 179998
22.2%
A 89999
11.1%
r 89999
11.1%
g 89999
11.1%
e 89999
11.1%
t 89999
11.1%
i 89999
11.1%
a 89999
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 809991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 179998
22.2%
A 89999
11.1%
r 89999
11.1%
g 89999
11.1%
e 89999
11.1%
t 89999
11.1%
i 89999
11.1%
a 89999
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 809991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 179998
22.2%
A 89999
11.1%
r 89999
11.1%
g 89999
11.1%
e 89999
11.1%
t 89999
11.1%
i 89999
11.1%
a 89999
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 809991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 179998
22.2%
A 89999
11.1%
r 89999
11.1%
g 89999
11.1%
e 89999
11.1%
t 89999
11.1%
i 89999
11.1%
a 89999
11.1%

seller_address_state.name
Categorical

High correlation  Imbalance 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
Capital Federal
52143 
Buenos Aires
31482 
Santa Fe
 
2398
Córdoba
 
1727
Mendoza
 
400
Other values (20)
 
1850

Length

Max length19
Median length15
Mean length13.426256
Min length0

Characters and Unicode

Total characters1208363
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCapital Federal
2nd rowCapital Federal
3rd rowCapital Federal
4th rowCapital Federal
5th rowBuenos Aires

Common Values

ValueCountFrequency (%)
Capital Federal 52143
57.9%
Buenos Aires 31482
35.0%
Santa Fe 2398
 
2.7%
Córdoba 1727
 
1.9%
Mendoza 400
 
0.4%
Chubut 335
 
0.4%
Entre Ríos 249
 
0.3%
Tucumán 214
 
0.2%
San Juan 132
 
0.1%
Salta 131
 
0.1%
Other values (15) 789
 
0.9%

Length

2025-02-21T23:10:03.416777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
capital 52143
29.5%
federal 52143
29.5%
buenos 31482
17.8%
aires 31482
17.8%
santa 2420
 
1.4%
fe 2398
 
1.4%
córdoba 1727
 
1.0%
mendoza 400
 
0.2%
chubut 335
 
0.2%
entre 249
 
0.1%
Other values (24) 1979
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 170927
14.1%
a 164459
13.6%
l 104453
 
8.6%
86759
 
7.2%
r 86056
 
7.1%
i 84087
 
7.0%
s 63677
 
5.3%
t 55460
 
4.6%
F 54579
 
4.5%
C 54426
 
4.5%
Other values (29) 283480
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1208363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 170927
14.1%
a 164459
13.6%
l 104453
 
8.6%
86759
 
7.2%
r 86056
 
7.1%
i 84087
 
7.0%
s 63677
 
5.3%
t 55460
 
4.6%
F 54579
 
4.5%
C 54426
 
4.5%
Other values (29) 283480
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1208363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 170927
14.1%
a 164459
13.6%
l 104453
 
8.6%
86759
 
7.2%
r 86056
 
7.1%
i 84087
 
7.0%
s 63677
 
5.3%
t 55460
 
4.6%
F 54579
 
4.5%
C 54426
 
4.5%
Other values (29) 283480
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1208363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 170927
14.1%
a 164459
13.6%
l 104453
 
8.6%
86759
 
7.2%
r 86056
 
7.1%
i 84087
 
7.0%
s 63677
 
5.3%
t 55460
 
4.6%
F 54579
 
4.5%
C 54426
 
4.5%
Other values (29) 283480
23.5%
Distinct3480
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-02-21T23:10:03.824674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length104
Median length70
Mean length10.536811
Min length0

Characters and Unicode

Total characters948313
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1616 ?
Unique (%)1.8%

Sample

1st rowSan Cristóbal
2nd rowBuenos Aires
3rd rowBoedo
4th rowFloresta
5th rowTres de febrero
ValueCountFrequency (%)
buenos 7074
 
4.8%
aires 7064
 
4.8%
capital 6955
 
4.7%
san 6687
 
4.5%
villa 6350
 
4.3%
federal 6316
 
4.3%
caba 5555
 
3.7%
de 3477
 
2.3%
palermo 3255
 
2.2%
caballito 3005
 
2.0%
Other values (1656) 92428
62.4%
2025-02-21T23:10:04.424990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 115900
 
12.2%
e 81985
 
8.6%
l 65238
 
6.9%
r 61918
 
6.5%
o 60509
 
6.4%
58561
 
6.2%
i 45590
 
4.8%
n 40753
 
4.3%
s 39674
 
4.2%
A 31032
 
3.3%
Other values (86) 347153
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 948313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 115900
 
12.2%
e 81985
 
8.6%
l 65238
 
6.9%
r 61918
 
6.5%
o 60509
 
6.4%
58561
 
6.2%
i 45590
 
4.8%
n 40753
 
4.3%
s 39674
 
4.2%
A 31032
 
3.3%
Other values (86) 347153
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 948313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 115900
 
12.2%
e 81985
 
8.6%
l 65238
 
6.9%
r 61918
 
6.5%
o 60509
 
6.4%
58561
 
6.2%
i 45590
 
4.8%
n 40753
 
4.3%
s 39674
 
4.2%
A 31032
 
3.3%
Other values (86) 347153
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 948313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 115900
 
12.2%
e 81985
 
8.6%
l 65238
 
6.9%
r 61918
 
6.5%
o 60509
 
6.4%
58561
 
6.2%
i 45590
 
4.8%
n 40753
 
4.3%
s 39674
 
4.2%
A 31032
 
3.3%
Other values (86) 347153
36.6%

warranty
Text

Missing 

Distinct9534
Distinct (%)27.1%
Missing54757
Missing (%)60.8%
Memory size7.8 MiB
2025-02-21T23:10:04.785555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length266
Median length246
Mean length39.638084
Min length1

Characters and Unicode

Total characters1396965
Distinct characters131
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6908 ?
Unique (%)19.6%

Sample

1st rowNUESTRA REPUTACION
2nd rowMI REPUTACION.
3rd row
4th row1 Ano
5th rowVACIOS sin utilizar
ValueCountFrequency (%)
de 16448
 
7.1%
garantía 8720
 
3.8%
el 5770
 
2.5%
sin 5719
 
2.5%
5269
 
2.3%
en 5172
 
2.2%
por 5133
 
2.2%
la 5068
 
2.2%
y 4702
 
2.0%
garantia 4567
 
2.0%
Other values (6609) 165961
71.4%
2025-02-21T23:10:05.392809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
198839
 
14.2%
a 105383
 
7.5%
e 87525
 
6.3%
o 66702
 
4.8%
n 59109
 
4.2%
r 57834
 
4.1%
i 56447
 
4.0%
s 52912
 
3.8%
t 43791
 
3.1%
A 43732
 
3.1%
Other values (121) 624691
44.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1396965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
198839
 
14.2%
a 105383
 
7.5%
e 87525
 
6.3%
o 66702
 
4.8%
n 59109
 
4.2%
r 57834
 
4.1%
i 56447
 
4.0%
s 52912
 
3.8%
t 43791
 
3.1%
A 43732
 
3.1%
Other values (121) 624691
44.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1396965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
198839
 
14.2%
a 105383
 
7.5%
e 87525
 
6.3%
o 66702
 
4.8%
n 59109
 
4.2%
r 57834
 
4.1%
i 56447
 
4.0%
s 52912
 
3.8%
t 43791
 
3.1%
A 43732
 
3.1%
Other values (121) 624691
44.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1396965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
198839
 
14.2%
a 105383
 
7.5%
e 87525
 
6.3%
o 66702
 
4.8%
n 59109
 
4.2%
r 57834
 
4.1%
i 56447
 
4.0%
s 52912
 
3.8%
t 43791
 
3.1%
A 43732
 
3.1%
Other values (121) 624691
44.7%

sub_status
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB

sub_status_0
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.3%
Missing89109
Missing (%)99.0%
Memory size5.5 MiB
suspended
871 
expired
 
13
deleted
 
7

Length

Max length9
Median length9
Mean length8.9551066
Min length7

Characters and Unicode

Total characters7979
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsuspended
2nd rowsuspended
3rd rowsuspended
4th rowsuspended
5th rowsuspended

Common Values

ValueCountFrequency (%)
suspended 871
 
1.0%
expired 13
 
< 0.1%
deleted 7
 
< 0.1%
(Missing) 89109
99.0%

Length

2025-02-21T23:10:05.541731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:05.647479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
suspended 871
97.8%
expired 13
 
1.5%
deleted 7
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e 1789
22.4%
d 1769
22.2%
s 1742
21.8%
p 884
11.1%
u 871
10.9%
n 871
10.9%
x 13
 
0.2%
i 13
 
0.2%
r 13
 
0.2%
l 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7979
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1789
22.4%
d 1769
22.2%
s 1742
21.8%
p 884
11.1%
u 871
10.9%
n 871
10.9%
x 13
 
0.2%
i 13
 
0.2%
r 13
 
0.2%
l 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7979
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1789
22.4%
d 1769
22.2%
s 1742
21.8%
p 884
11.1%
u 871
10.9%
n 871
10.9%
x 13
 
0.2%
i 13
 
0.2%
r 13
 
0.2%
l 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7979
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1789
22.4%
d 1769
22.2%
s 1742
21.8%
p 884
11.1%
u 871
10.9%
n 871
10.9%
x 13
 
0.2%
i 13
 
0.2%
r 13
 
0.2%
l 7
 
0.1%

condition
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
new
48352 
used
41648 

Length

Max length4
Median length3
Mean length3.4627556
Min length3

Characters and Unicode

Total characters311648
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownew
2nd rowused
3rd rowused
4th rownew
5th rowused

Common Values

ValueCountFrequency (%)
new 48352
53.7%
used 41648
46.3%

Length

2025-02-21T23:10:05.771555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:05.867335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new 48352
53.7%
used 41648
46.3%

Most occurring characters

ValueCountFrequency (%)
e 90000
28.9%
n 48352
15.5%
w 48352
15.5%
u 41648
13.4%
s 41648
13.4%
d 41648
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 311648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 90000
28.9%
n 48352
15.5%
w 48352
15.5%
u 41648
13.4%
s 41648
13.4%
d 41648
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 311648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 90000
28.9%
n 48352
15.5%
w 48352
15.5%
u 41648
13.4%
s 41648
13.4%
d 41648
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 311648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 90000
28.9%
n 48352
15.5%
w 48352
15.5%
u 41648
13.4%
s 41648
13.4%
d 41648
13.4%

deal_ids
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB

deal_ids_0
Categorical

High correlation  Missing 

Distinct30
Distinct (%)13.8%
Missing89783
Missing (%)99.8%
Memory size5.5 MiB
MOSH6
52 
WGPLA
36 
ABQ1I
27 
15H9O
26 
LZFND
22 
Other values (25)
54 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1085
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)5.5%

Sample

1st row15H9O
2nd rowJXLIQ
3rd rowLZFND
4th rowWGPLA
5th rowMOSH6

Common Values

ValueCountFrequency (%)
MOSH6 52
 
0.1%
WGPLA 36
 
< 0.1%
ABQ1I 27
 
< 0.1%
15H9O 26
 
< 0.1%
LZFND 22
 
< 0.1%
JXLIQ 6
 
< 0.1%
MISCR 6
 
< 0.1%
CHMGI 6
 
< 0.1%
R99GD 5
 
< 0.1%
AZEBL 3
 
< 0.1%
Other values (20) 28
 
< 0.1%
(Missing) 89783
99.8%

Length

2025-02-21T23:10:05.976069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mosh6 52
24.0%
wgpla 36
16.6%
abq1i 27
12.4%
15h9o 26
12.0%
lzfnd 22
10.1%
jxliq 6
 
2.8%
miscr 6
 
2.8%
chmgi 6
 
2.8%
r99gd 5
 
2.3%
azebl 3
 
1.4%
Other values (20) 28
12.9%

Most occurring characters

ValueCountFrequency (%)
H 87
 
8.0%
O 82
 
7.6%
L 71
 
6.5%
A 71
 
6.5%
M 69
 
6.4%
S 61
 
5.6%
6 55
 
5.1%
1 55
 
5.1%
I 53
 
4.9%
G 52
 
4.8%
Other values (25) 429
39.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 87
 
8.0%
O 82
 
7.6%
L 71
 
6.5%
A 71
 
6.5%
M 69
 
6.4%
S 61
 
5.6%
6 55
 
5.1%
1 55
 
5.1%
I 53
 
4.9%
G 52
 
4.8%
Other values (25) 429
39.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 87
 
8.0%
O 82
 
7.6%
L 71
 
6.5%
A 71
 
6.5%
M 69
 
6.4%
S 61
 
5.6%
6 55
 
5.1%
1 55
 
5.1%
I 53
 
4.9%
G 52
 
4.8%
Other values (25) 429
39.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 87
 
8.0%
O 82
 
7.6%
L 71
 
6.5%
A 71
 
6.5%
M 69
 
6.4%
S 61
 
5.6%
6 55
 
5.1%
1 55
 
5.1%
I 53
 
4.9%
G 52
 
4.8%
Other values (25) 429
39.5%

base_price
Real number (ℝ)

High correlation  Skewed 

Distinct9594
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57813.408
Minimum0.84
Maximum2.2222222 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:06.142923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.84
5-th percentile35
Q190
median250
Q3800
95-th percentile7500
Maximum2.2222222 × 109
Range2.2222222 × 109
Interquartile range (IQR)710

Descriptive statistics

Standard deviation9089554.8
Coefficient of variation (CV)157.22226
Kurtosis44652.94
Mean57813.408
Median Absolute Deviation (MAD)198
Skewness202.99257
Sum5.2032067 × 109
Variance8.2620006 × 1013
MonotonicityNot monotonic
2025-02-21T23:10:06.341813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 2920
 
3.2%
100 2562
 
2.8%
150 2242
 
2.5%
60 1859
 
2.1%
200 1789
 
2.0%
80 1705
 
1.9%
250 1581
 
1.8%
120 1511
 
1.7%
40 1508
 
1.7%
70 1498
 
1.7%
Other values (9584) 70825
78.7%
ValueCountFrequency (%)
0.84 1
 
< 0.1%
1 97
0.1%
1.2 1
 
< 0.1%
1.5 1
 
< 0.1%
1.8 1
 
< 0.1%
1.82 1
 
< 0.1%
1.87 1
 
< 0.1%
2 8
 
< 0.1%
2.21 1
 
< 0.1%
2.25 1
 
< 0.1%
ValueCountFrequency (%)
2222222222 1
 
< 0.1%
1111111111 2
< 0.1%
123456789 1
 
< 0.1%
112111111 1
 
< 0.1%
11111111 3
< 0.1%
9000000 1
 
< 0.1%
8888888 1
 
< 0.1%
6500000 1
 
< 0.1%
5330000 1
 
< 0.1%
3956000 1
 
< 0.1%

shipping_local_pick_up
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size88.0 KiB
True
71577 
False
18423 
ValueCountFrequency (%)
True 71577
79.5%
False 18423
 
20.5%
2025-02-21T23:10:06.463555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

shipping_methods
Unsupported

Missing  Rejected  Unsupported 

Missing2690
Missing (%)3.0%
Memory size5.4 MiB

shipping_tags
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.5 MiB

shipping_free_shipping
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size88.0 KiB
False
87303 
True
 
2697
ValueCountFrequency (%)
False 87303
97.0%
True 2697
 
3.0%
2025-02-21T23:10:06.530751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

shipping_mode
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
me2
46059 
not_specified
40725 
custom
 
3130
me1
 
86

Length

Max length13
Median length3
Mean length7.6293333
Min length3

Characters and Unicode

Total characters686640
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot_specified
2nd rowme2
3rd rowme2
4th rowme2
5th rownot_specified

Common Values

ValueCountFrequency (%)
me2 46059
51.2%
not_specified 40725
45.2%
custom 3130
 
3.5%
me1 86
 
0.1%

Length

2025-02-21T23:10:06.639553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:06.745329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
me2 46059
51.2%
not_specified 40725
45.2%
custom 3130
 
3.5%
me1 86
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 127595
18.6%
i 81450
11.9%
m 49275
 
7.2%
2 46059
 
6.7%
o 43855
 
6.4%
t 43855
 
6.4%
s 43855
 
6.4%
c 43855
 
6.4%
n 40725
 
5.9%
_ 40725
 
5.9%
Other values (5) 125391
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 686640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 127595
18.6%
i 81450
11.9%
m 49275
 
7.2%
2 46059
 
6.7%
o 43855
 
6.4%
t 43855
 
6.4%
s 43855
 
6.4%
c 43855
 
6.4%
n 40725
 
5.9%
_ 40725
 
5.9%
Other values (5) 125391
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 686640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 127595
18.6%
i 81450
11.9%
m 49275
 
7.2%
2 46059
 
6.7%
o 43855
 
6.4%
t 43855
 
6.4%
s 43855
 
6.4%
c 43855
 
6.4%
n 40725
 
5.9%
_ 40725
 
5.9%
Other values (5) 125391
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 686640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 127595
18.6%
i 81450
11.9%
m 49275
 
7.2%
2 46059
 
6.7%
o 43855
 
6.4%
t 43855
 
6.4%
s 43855
 
6.4%
c 43855
 
6.4%
n 40725
 
5.9%
_ 40725
 
5.9%
Other values (5) 125391
18.3%

shipping_dimensions
Text

Missing 

Distinct13
Distinct (%)59.1%
Missing89978
Missing (%)> 99.9%
Memory size2.7 MiB
2025-02-21T23:10:06.923838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.909091
Min length11

Characters and Unicode

Total characters262
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)31.8%

Sample

1st row5x25x25,150
2nd row33x20x12,80
3rd row30x30x30,650
4th row33x20x12,500
5th row33x20x12,400
ValueCountFrequency (%)
33x20x12,300 4
18.2%
30x30x30,650 3
13.6%
33x20x12,80 2
9.1%
33x20x12,400 2
9.1%
33x20x12,1000 2
9.1%
33x20x12,100 2
9.1%
5x25x25,150 1
 
4.5%
33x20x12,500 1
 
4.5%
10x10x20,700 1
 
4.5%
10x20x20,350 1
 
4.5%
Other values (3) 3
13.6%
2025-02-21T23:10:07.271429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 69
26.3%
3 44
16.8%
x 44
16.8%
2 34
13.0%
1 23
 
8.8%
, 22
 
8.4%
5 14
 
5.3%
6 5
 
1.9%
8 3
 
1.1%
4 3
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 69
26.3%
3 44
16.8%
x 44
16.8%
2 34
13.0%
1 23
 
8.8%
, 22
 
8.4%
5 14
 
5.3%
6 5
 
1.9%
8 3
 
1.1%
4 3
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 69
26.3%
3 44
16.8%
x 44
16.8%
2 34
13.0%
1 23
 
8.8%
, 22
 
8.4%
5 14
 
5.3%
6 5
 
1.9%
8 3
 
1.1%
4 3
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 69
26.3%
3 44
16.8%
x 44
16.8%
2 34
13.0%
1 23
 
8.8%
, 22
 
8.4%
5 14
 
5.3%
6 5
 
1.9%
8 3
 
1.1%
4 3
 
1.1%

shipping_free_methods
Unsupported

Missing  Rejected  Unsupported 

Missing87311
Missing (%)97.0%
Memory size2.9 MiB

non_mercado_pago_payment_methods_description
Categorical

High correlation  Imbalance  Missing 

Distinct9
Distinct (%)< 0.1%
Missing27531
Missing (%)30.6%
Memory size5.6 MiB
Efectivo
59172 
Acordar con el comprador
 
1032
MercadoPago
 
646
Transferencia bancaria
 
574
American Express
 
533
Other values (4)
 
512

Length

Max length24
Median length8
Mean length8.572572
Min length4

Characters and Unicode

Total characters535520
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowEfectivo
2nd rowEfectivo
3rd rowEfectivo
4th rowEfectivo
5th rowEfectivo

Common Values

ValueCountFrequency (%)
Efectivo 59172
65.7%
Acordar con el comprador 1032
 
1.1%
MercadoPago 646
 
0.7%
Transferencia bancaria 574
 
0.6%
American Express 533
 
0.6%
Tarjeta de crédito 497
 
0.6%
Giro postal 12
 
< 0.1%
Contra reembolso 2
 
< 0.1%
Visa 1
 
< 0.1%
(Missing) 27531
30.6%

Length

2025-02-21T23:10:07.438020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:07.592933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
efectivo 59172
87.4%
acordar 1032
 
1.5%
con 1032
 
1.5%
el 1032
 
1.5%
comprador 1032
 
1.5%
mercadopago 646
 
1.0%
transferencia 574
 
0.8%
bancaria 574
 
0.8%
express 533
 
0.8%
american 533
 
0.8%
Other values (8) 1520
 
2.2%

Most occurring characters

ValueCountFrequency (%)
o 65119
12.2%
c 65092
12.2%
e 64062
12.0%
i 61363
11.5%
t 60180
11.2%
f 59746
11.2%
E 59705
11.1%
v 59172
11.0%
r 8572
 
1.6%
a 7768
 
1.5%
Other values (19) 24741
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 535520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 65119
12.2%
c 65092
12.2%
e 64062
12.0%
i 61363
11.5%
t 60180
11.2%
f 59746
11.2%
E 59705
11.1%
v 59172
11.0%
r 8572
 
1.6%
a 7768
 
1.5%
Other values (19) 24741
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 535520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 65119
12.2%
c 65092
12.2%
e 64062
12.0%
i 61363
11.5%
t 60180
11.2%
f 59746
11.2%
E 59705
11.1%
v 59172
11.0%
r 8572
 
1.6%
a 7768
 
1.5%
Other values (19) 24741
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 535520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 65119
12.2%
c 65092
12.2%
e 64062
12.0%
i 61363
11.5%
t 60180
11.2%
f 59746
11.2%
E 59705
11.1%
v 59172
11.0%
r 8572
 
1.6%
a 7768
 
1.5%
Other values (19) 24741
 
4.6%

non_mercado_pago_payment_methods_id
Categorical

High correlation  Imbalance  Missing 

Distinct9
Distinct (%)< 0.1%
Missing27531
Missing (%)30.6%
Memory size5.4 MiB
MLAMO
59172 
MLAWC
 
1032
MLAMP
 
646
MLATB
 
574
MLAAM
 
533
Other values (4)
 
512

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters312345
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMLAMO
2nd rowMLAMO
3rd rowMLAMO
4th rowMLAMO
5th rowMLAMO

Common Values

ValueCountFrequency (%)
MLAMO 59172
65.7%
MLAWC 1032
 
1.1%
MLAMP 646
 
0.7%
MLATB 574
 
0.6%
MLAAM 533
 
0.6%
MLAOT 497
 
0.6%
MLAWT 12
 
< 0.1%
MLACD 2
 
< 0.1%
MLAVS 1
 
< 0.1%
(Missing) 27531
30.6%

Length

2025-02-21T23:10:07.781114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:07.909210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mlamo 59172
94.7%
mlawc 1032
 
1.7%
mlamp 646
 
1.0%
mlatb 574
 
0.9%
mlaam 533
 
0.9%
mlaot 497
 
0.8%
mlawt 12
 
< 0.1%
mlacd 2
 
< 0.1%
mlavs 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 122820
39.3%
A 63002
20.2%
L 62469
20.0%
O 59669
19.1%
T 1083
 
0.3%
W 1044
 
0.3%
C 1034
 
0.3%
P 646
 
0.2%
B 574
 
0.2%
D 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 312345
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 122820
39.3%
A 63002
20.2%
L 62469
20.0%
O 59669
19.1%
T 1083
 
0.3%
W 1044
 
0.3%
C 1034
 
0.3%
P 646
 
0.2%
B 574
 
0.2%
D 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 312345
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 122820
39.3%
A 63002
20.2%
L 62469
20.0%
O 59669
19.1%
T 1083
 
0.3%
W 1044
 
0.3%
C 1034
 
0.3%
P 646
 
0.2%
B 574
 
0.2%
D 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 312345
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 122820
39.3%
A 63002
20.2%
L 62469
20.0%
O 59669
19.1%
T 1083
 
0.3%
W 1044
 
0.3%
C 1034
 
0.3%
P 646
 
0.2%
B 574
 
0.2%
D 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

non_mercado_pago_payment_methods_type
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing27531
Missing (%)30.6%
Memory size5.1 MiB
G
61438 
C
 
534
N
 
497

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters62469
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowG
5th rowG

Common Values

ValueCountFrequency (%)
G 61438
68.3%
C 534
 
0.6%
N 497
 
0.6%
(Missing) 27531
30.6%

Length

2025-02-21T23:10:08.082252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:08.178995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
g 61438
98.3%
c 534
 
0.9%
n 497
 
0.8%

Most occurring characters

ValueCountFrequency (%)
G 61438
98.3%
C 534
 
0.9%
N 497
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 61438
98.3%
C 534
 
0.9%
N 497
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 61438
98.3%
C 534
 
0.9%
N 497
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 61438
98.3%
C 534
 
0.9%
N 497
 
0.8%

non_mercado_pago_payment_methods
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB

seller_id
Real number (ℝ)

Distinct33281
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4873856 × 109
Minimum1.0003195 × 109
Maximum9.9998527 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:08.324802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0003195 × 109
5-th percentile1.4533779 × 109
Q13.1848746 × 109
median5.512496 × 109
Q37.7049297 × 109
95-th percentile9.539384 × 109
Maximum9.9998527 × 109
Range8.9995333 × 109
Interquartile range (IQR)4.5200551 × 109

Descriptive statistics

Standard deviation2.58714 × 109
Coefficient of variation (CV)0.47147042
Kurtosis-1.2024066
Mean5.4873856 × 109
Median Absolute Deviation (MAD)2.2388545 × 109
Skewness-0.016330076
Sum4.938647 × 1014
Variance6.6932932 × 1018
MonotonicityNot monotonic
2025-02-21T23:10:08.535068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5248662274 850
 
0.9%
2015548469 654
 
0.7%
7704929703 424
 
0.5%
4631246902 408
 
0.5%
8612126795 318
 
0.4%
2266082781 302
 
0.3%
6972484560 259
 
0.3%
6846806944 251
 
0.3%
4248718919 244
 
0.3%
1387735603 240
 
0.3%
Other values (33271) 86050
95.6%
ValueCountFrequency (%)
1000319478 1
 
< 0.1%
1000326044 1
 
< 0.1%
1000995831 1
 
< 0.1%
1001280112 1
 
< 0.1%
1001468959 3
< 0.1%
1001604238 4
< 0.1%
1001804662 1
 
< 0.1%
1001825325 1
 
< 0.1%
1001829760 2
< 0.1%
1002732100 1
 
< 0.1%
ValueCountFrequency (%)
9999852737 1
 
< 0.1%
9999825764 5
< 0.1%
9999582201 10
< 0.1%
9998845396 1
 
< 0.1%
9998380326 1
 
< 0.1%
9998056162 1
 
< 0.1%
9997890836 1
 
< 0.1%
9997797642 1
 
< 0.1%
9996404064 1
 
< 0.1%
9995833164 1
 
< 0.1%

variations
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB

variations_attribute_combinations
Unsupported

Missing  Rejected  Unsupported 

Missing82626
Missing (%)91.8%
Memory size3.2 MiB
Distinct131
Distinct (%)100.0%
Missing89869
Missing (%)99.9%
Memory size2.8 MiB
2025-02-21T23:10:08.873664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length24
Mean length15.198473
Min length0

Characters and Unicode

Total characters1991
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)100.0%

Sample

1st row23225021-11-l
2nd rowmuaa_242009018-NEGRO-7
3rd row480120056891004M
4th row0150701360V27BR28
5th rowstreet_1811001-Gris-3/42
ValueCountFrequency (%)
82691 1
 
0.8%
90136 1
 
0.8%
muaa_242009018-negro-7 1
 
0.8%
480120056891004m 1
 
0.8%
0150701360v27br28 1
 
0.8%
street_1811001-gris-3/42 1
 
0.8%
fila_51j330a-117-40 1
 
0.8%
10434518917101m_1 1
 
0.8%
119431 1
 
0.8%
promesse_72045-000br-l 1
 
0.8%
Other values (121) 121
92.4%
2025-02-21T23:10:09.561023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 310
15.6%
1 200
 
10.0%
2 124
 
6.2%
- 119
 
6.0%
4 110
 
5.5%
8 84
 
4.2%
3 84
 
4.2%
5 82
 
4.1%
9 65
 
3.3%
7 65
 
3.3%
Other values (51) 748
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 310
15.6%
1 200
 
10.0%
2 124
 
6.2%
- 119
 
6.0%
4 110
 
5.5%
8 84
 
4.2%
3 84
 
4.2%
5 82
 
4.1%
9 65
 
3.3%
7 65
 
3.3%
Other values (51) 748
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 310
15.6%
1 200
 
10.0%
2 124
 
6.2%
- 119
 
6.0%
4 110
 
5.5%
8 84
 
4.2%
3 84
 
4.2%
5 82
 
4.1%
9 65
 
3.3%
7 65
 
3.3%
Other values (51) 748
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 310
15.6%
1 200
 
10.0%
2 124
 
6.2%
- 119
 
6.0%
4 110
 
5.5%
8 84
 
4.2%
3 84
 
4.2%
5 82
 
4.1%
9 65
 
3.3%
7 65
 
3.3%
Other values (51) 748
37.6%

variations_picture_ids
Unsupported

Missing  Rejected  Unsupported 

Missing82626
Missing (%)91.8%
Memory size3.3 MiB

variations_sold_quantity
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct17
Distinct (%)0.2%
Missing82626
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean0.11825332
Minimum0
Maximum61
Zeros6966
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:10.178402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum61
Range61
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1146706
Coefficient of variation (CV)9.4261252
Kurtosis1455.3156
Mean0.11825332
Median Absolute Deviation (MAD)0
Skewness32.39484
Sum872
Variance1.2424906
MonotonicityNot monotonic
2025-02-21T23:10:10.923961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 6966
 
7.7%
1 283
 
0.3%
2 58
 
0.1%
3 28
 
< 0.1%
4 12
 
< 0.1%
5 8
 
< 0.1%
8 3
 
< 0.1%
10 3
 
< 0.1%
6 3
 
< 0.1%
9 2
 
< 0.1%
Other values (7) 8
 
< 0.1%
(Missing) 82626
91.8%
ValueCountFrequency (%)
0 6966
7.7%
1 283
 
0.3%
2 58
 
0.1%
3 28
 
< 0.1%
4 12
 
< 0.1%
5 8
 
< 0.1%
6 3
 
< 0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
61 1
 
< 0.1%
36 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
21 1
 
< 0.1%
17 1
 
< 0.1%
13 2
< 0.1%
10 3
< 0.1%
9 2
< 0.1%
8 3
< 0.1%

variations_available_quantity
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct73
Distinct (%)1.0%
Missing82626
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean7.0965555
Minimum0
Maximum9999
Zeros232
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:11.137319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile10
Maximum9999
Range9999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation168.22783
Coefficient of variation (CV)23.705561
Kurtosis3376.787
Mean7.0965555
Median Absolute Deviation (MAD)0
Skewness57.063297
Sum52330
Variance28300.603
MonotonicityNot monotonic
2025-02-21T23:10:11.533278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5688
 
6.3%
2 410
 
0.5%
0 232
 
0.3%
10 208
 
0.2%
5 179
 
0.2%
3 166
 
0.2%
4 101
 
0.1%
20 53
 
0.1%
6 45
 
0.1%
50 34
 
< 0.1%
Other values (63) 258
 
0.3%
(Missing) 82626
91.8%
ValueCountFrequency (%)
0 232
 
0.3%
1 5688
6.3%
2 410
 
0.5%
3 166
 
0.2%
4 101
 
0.1%
5 179
 
0.2%
6 45
 
0.1%
7 22
 
< 0.1%
8 21
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
9999 2
< 0.1%
1000 1
< 0.1%
999 2
< 0.1%
998 1
< 0.1%
990 1
< 0.1%
955 1
< 0.1%
900 1
< 0.1%
748 1
< 0.1%
664 1
< 0.1%
500 1
< 0.1%

variations_id
Real number (ℝ)

High correlation  Missing 

Distinct7374
Distinct (%)100.0%
Missing82626
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean9.5632336 × 109
Minimum8.8302733 × 109
Maximum9.8070682 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:11.847925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.8302733 × 109
5-th percentile9.2964576 × 109
Q19.4253245 × 109
median9.5814757 × 109
Q39.7053164 × 109
95-th percentile9.7849287 × 109
Maximum9.8070682 × 109
Range9.7679492 × 108
Interquartile range (IQR)2.7999192 × 108

Descriptive statistics

Standard deviation1.6042922 × 108
Coefficient of variation (CV)0.016775625
Kurtosis-0.89667209
Mean9.5632336 × 109
Median Absolute Deviation (MAD)1.3574447 × 108
Skewness-0.30901637
Sum7.0519284 × 1013
Variance2.5737535 × 1016
MonotonicityNot monotonic
2025-02-21T23:10:12.229204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9415001293 1
 
< 0.1%
9474512345 1
 
< 0.1%
9803147049 1
 
< 0.1%
9348114962 1
 
< 0.1%
9539389785 1
 
< 0.1%
9453025329 1
 
< 0.1%
9295785370 1
 
< 0.1%
9314856593 1
 
< 0.1%
9479433344 1
 
< 0.1%
9666762953 1
 
< 0.1%
Other values (7364) 7364
 
8.2%
(Missing) 82626
91.8%
ValueCountFrequency (%)
8830273320 1
< 0.1%
8909589648 1
< 0.1%
8911055140 1
< 0.1%
8921461685 1
< 0.1%
8987183210 1
< 0.1%
8987210858 1
< 0.1%
9018728213 1
< 0.1%
9023635114 1
< 0.1%
9026966168 1
< 0.1%
9027127274 1
< 0.1%
ValueCountFrequency (%)
9807068245 1
< 0.1%
9806369133 1
< 0.1%
9806307860 1
< 0.1%
9805614670 1
< 0.1%
9805547813 1
< 0.1%
9805526927 1
< 0.1%
9805408918 1
< 0.1%
9805384711 1
< 0.1%
9805370478 1
< 0.1%
9805356311 1
< 0.1%

variations_price
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct833
Distinct (%)11.3%
Missing82626
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean2165.0328
Minimum2
Maximum11111111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:12.595446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile60
Q1169.99
median300
Q3599
95-th percentile1600
Maximum11111111
Range11111109
Interquartile range (IQR)429.01

Descriptive statistics

Standard deviation130030.51
Coefficient of variation (CV)60.059372
Kurtosis7229.0692
Mean2165.0328
Median Absolute Deviation (MAD)170.01
Skewness84.681416
Sum15964952
Variance1.6907934 × 1010
MonotonicityNot monotonic
2025-02-21T23:10:12.995255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 294
 
0.3%
200 292
 
0.3%
150 279
 
0.3%
300 258
 
0.3%
350 219
 
0.2%
500 191
 
0.2%
100 190
 
0.2%
400 172
 
0.2%
450 149
 
0.2%
50 126
 
0.1%
Other values (823) 5204
 
5.8%
(Missing) 82626
91.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
18 1
 
< 0.1%
20 12
< 0.1%
22 1
 
< 0.1%
23.5 1
 
< 0.1%
25 7
< 0.1%
27 4
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
11111111 1
< 0.1%
1111111 1
< 0.1%
15000 1
< 0.1%
14400 1
< 0.1%
13000 1
< 0.1%
10000 1
< 0.1%
8800 1
< 0.1%
8500.01 1
< 0.1%
8500 1
< 0.1%
7500 1
< 0.1%

site_id
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
MLA
90000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters270000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMLA
2nd rowMLA
3rd rowMLA
4th rowMLA
5th rowMLA

Common Values

ValueCountFrequency (%)
MLA 90000
100.0%

Length

2025-02-21T23:10:13.234041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:13.343132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mla 90000
100.0%

Most occurring characters

ValueCountFrequency (%)
M 90000
33.3%
L 90000
33.3%
A 90000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 270000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 90000
33.3%
L 90000
33.3%
A 90000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 270000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 90000
33.3%
L 90000
33.3%
A 90000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 270000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 90000
33.3%
L 90000
33.3%
A 90000
33.3%

listing_type_id
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 MiB
bronze
56904 
free
19260 
silver
8195 
gold_special
 
2693
gold
 
2170
Other values (2)
 
778

Length

Max length12
Median length6
Mean length5.7546
Min length4

Characters and Unicode

Total characters517914
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbronze
2nd rowsilver
3rd rowbronze
4th rowsilver
5th rowbronze

Common Values

ValueCountFrequency (%)
bronze 56904
63.2%
free 19260
 
21.4%
silver 8195
 
9.1%
gold_special 2693
 
3.0%
gold 2170
 
2.4%
gold_premium 765
 
0.9%
gold_pro 13
 
< 0.1%

Length

2025-02-21T23:10:13.479157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:13.620218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bronze 56904
63.2%
free 19260
 
21.4%
silver 8195
 
9.1%
gold_special 2693
 
3.0%
gold 2170
 
2.4%
gold_premium 765
 
0.9%
gold_pro 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 107077
20.7%
r 85137
16.4%
o 62558
12.1%
b 56904
11.0%
n 56904
11.0%
z 56904
11.0%
f 19260
 
3.7%
l 16529
 
3.2%
i 11653
 
2.2%
s 10888
 
2.1%
Other values (9) 34100
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 517914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 107077
20.7%
r 85137
16.4%
o 62558
12.1%
b 56904
11.0%
n 56904
11.0%
z 56904
11.0%
f 19260
 
3.7%
l 16529
 
3.2%
i 11653
 
2.2%
s 10888
 
2.1%
Other values (9) 34100
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 517914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 107077
20.7%
r 85137
16.4%
o 62558
12.1%
b 56904
11.0%
n 56904
11.0%
z 56904
11.0%
f 19260
 
3.7%
l 16529
 
3.2%
i 11653
 
2.2%
s 10888
 
2.1%
Other values (9) 34100
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 517914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 107077
20.7%
r 85137
16.4%
o 62558
12.1%
b 56904
11.0%
n 56904
11.0%
z 56904
11.0%
f 19260
 
3.7%
l 16529
 
3.2%
i 11653
 
2.2%
s 10888
 
2.1%
Other values (9) 34100
 
6.6%

price
Real number (ℝ)

High correlation  Skewed 

Distinct9595
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57813.518
Minimum0.84
Maximum2.2222222 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:13.823579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.84
5-th percentile35
Q190
median250
Q3800
95-th percentile7500
Maximum2.2222222 × 109
Range2.2222222 × 109
Interquartile range (IQR)710

Descriptive statistics

Standard deviation9089554.8
Coefficient of variation (CV)157.22196
Kurtosis44652.94
Mean57813.518
Median Absolute Deviation (MAD)198
Skewness202.99257
Sum5.2032166 × 109
Variance8.2620006 × 1013
MonotonicityNot monotonic
2025-02-21T23:10:14.015266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 2920
 
3.2%
100 2562
 
2.8%
150 2242
 
2.5%
60 1859
 
2.1%
200 1789
 
2.0%
80 1705
 
1.9%
250 1581
 
1.8%
120 1511
 
1.7%
40 1508
 
1.7%
70 1498
 
1.7%
Other values (9585) 70825
78.7%
ValueCountFrequency (%)
0.84 1
 
< 0.1%
1 77
0.1%
1.2 1
 
< 0.1%
1.5 1
 
< 0.1%
1.8 1
 
< 0.1%
1.82 1
 
< 0.1%
1.87 1
 
< 0.1%
2 9
 
< 0.1%
2.21 1
 
< 0.1%
2.25 1
 
< 0.1%
ValueCountFrequency (%)
2222222222 1
 
< 0.1%
1111111111 2
< 0.1%
123456789 1
 
< 0.1%
112111111 1
 
< 0.1%
11111111 3
< 0.1%
9000000 1
 
< 0.1%
8888888 1
 
< 0.1%
6500000 1
 
< 0.1%
5330000 1
 
< 0.1%
3956000 1
 
< 0.1%

attributes
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB

attributes_value_id
Text

Missing 

Distinct146
Distinct (%)1.3%
Missing78850
Missing (%)87.6%
Memory size3.2 MiB
2025-02-21T23:10:14.234068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length30
Mean length13.146188
Min length0

Characters and Unicode

Total characters146580
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)0.6%

Sample

1st row
2nd rowSeason-Spring-Summer
3rd row
4th rowSeason-Spring-Summer
5th rowSeason-All-Season
ValueCountFrequency (%)
season-all-season 4021
46.9%
season-autumn-winter 2049
23.9%
season-spring-summer 849
 
9.9%
mla1744-usb-n 550
 
6.4%
mla1744-usb-y 254
 
3.0%
female 189
 
2.2%
male 110
 
1.3%
611eef5 45
 
0.5%
fa5c825 44
 
0.5%
mla1763-year-2014 42
 
0.5%
Other values (135) 417
 
4.9%
2025-02-21T23:10:14.567728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 16186
11.0%
n 15887
10.8%
e 14435
9.8%
S 13501
9.2%
a 11319
 
7.7%
s 10958
 
7.5%
o 10945
 
7.5%
l 8354
 
5.7%
A 7602
 
5.2%
u 4947
 
3.4%
Other values (45) 32446
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 146580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 16186
11.0%
n 15887
10.8%
e 14435
9.8%
S 13501
9.2%
a 11319
 
7.7%
s 10958
 
7.5%
o 10945
 
7.5%
l 8354
 
5.7%
A 7602
 
5.2%
u 4947
 
3.4%
Other values (45) 32446
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 146580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 16186
11.0%
n 15887
10.8%
e 14435
9.8%
S 13501
9.2%
a 11319
 
7.7%
s 10958
 
7.5%
o 10945
 
7.5%
l 8354
 
5.7%
A 7602
 
5.2%
u 4947
 
3.4%
Other values (45) 32446
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 146580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 16186
11.0%
n 15887
10.8%
e 14435
9.8%
S 13501
9.2%
a 11319
 
7.7%
s 10958
 
7.5%
o 10945
 
7.5%
l 8354
 
5.7%
A 7602
 
5.2%
u 4947
 
3.4%
Other values (45) 32446
22.1%

attributes_attribute_group_id
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing78850
Missing (%)87.6%
Memory size5.5 MiB
FIND
5832 
DFLT
4448 
SONIDO
838 
SECURITY
 
21
RANGCUOTA
 
11

Length

Max length9
Median length4
Mean length4.1627803
Min length4

Characters and Unicode

Total characters46415
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDFLT
2nd rowFIND
3rd rowDFLT
4th rowFIND
5th rowFIND

Common Values

ValueCountFrequency (%)
FIND 5832
 
6.5%
DFLT 4448
 
4.9%
SONIDO 838
 
0.9%
SECURITY 21
 
< 0.1%
RANGCUOTA 11
 
< 0.1%
(Missing) 78850
87.6%

Length

2025-02-21T23:10:14.697783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:14.814844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
find 5832
52.3%
dflt 4448
39.9%
sonido 838
 
7.5%
security 21
 
0.2%
rangcuota 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 11118
24.0%
F 10280
22.1%
I 6691
14.4%
N 6681
14.4%
T 4480
9.7%
L 4448
9.6%
O 1687
 
3.6%
S 859
 
1.9%
C 32
 
0.1%
U 32
 
0.1%
Other values (5) 107
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 11118
24.0%
F 10280
22.1%
I 6691
14.4%
N 6681
14.4%
T 4480
9.7%
L 4448
9.6%
O 1687
 
3.6%
S 859
 
1.9%
C 32
 
0.1%
U 32
 
0.1%
Other values (5) 107
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 11118
24.0%
F 10280
22.1%
I 6691
14.4%
N 6681
14.4%
T 4480
9.7%
L 4448
9.6%
O 1687
 
3.6%
S 859
 
1.9%
C 32
 
0.1%
U 32
 
0.1%
Other values (5) 107
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 11118
24.0%
F 10280
22.1%
I 6691
14.4%
N 6681
14.4%
T 4480
9.7%
L 4448
9.6%
O 1687
 
3.6%
S 859
 
1.9%
C 32
 
0.1%
U 32
 
0.1%
Other values (5) 107
 
0.2%

attributes_name
Categorical

High correlation  Imbalance  Missing 

Distinct23
Distinct (%)0.2%
Missing78850
Missing (%)87.6%
Memory size5.6 MiB
Season
6919 
Número de pieza
1467 
Entrada USB
807 
Superficie total (m²)
731 
Género
 
317
Other values (18)
909 

Length

Max length26
Median length6
Mean length8.856861
Min length3

Characters and Unicode

Total characters98754
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNúmero de pieza
2nd rowSeason
3rd rowNúmero pieza
4th rowSeason
5th rowSeason

Common Values

ValueCountFrequency (%)
Season 6919
 
7.7%
Número de pieza 1467
 
1.6%
Entrada USB 807
 
0.9%
Superficie total (m²) 731
 
0.8%
Género 317
 
0.4%
Año 261
 
0.3%
Número pieza 241
 
0.3%
Número de Pieza 63
 
0.1%
nro_pieza 61
 
0.1%
Ancho 55
 
0.1%
Other values (13) 228
 
0.3%
(Missing) 78850
87.6%

Length

2025-02-21T23:10:14.976411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
season 6919
40.5%
pieza 1771
 
10.4%
número 1771
 
10.4%
de 1690
 
9.9%
entrada 807
 
4.7%
usb 807
 
4.7%
superficie 731
 
4.3%
total 731
 
4.3%
731
 
4.3%
género 317
 
1.9%
Other values (23) 827
 
4.8%

Most occurring characters

ValueCountFrequency (%)
e 14300
14.5%
a 11394
11.5%
o 10397
10.5%
S 8457
 
8.6%
n 8388
 
8.5%
s 7035
 
7.1%
5952
 
6.0%
r 3861
 
3.9%
i 3615
 
3.7%
d 2645
 
2.7%
Other values (33) 22710
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14300
14.5%
a 11394
11.5%
o 10397
10.5%
S 8457
 
8.6%
n 8388
 
8.5%
s 7035
 
7.1%
5952
 
6.0%
r 3861
 
3.9%
i 3615
 
3.7%
d 2645
 
2.7%
Other values (33) 22710
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14300
14.5%
a 11394
11.5%
o 10397
10.5%
S 8457
 
8.6%
n 8388
 
8.5%
s 7035
 
7.1%
5952
 
6.0%
r 3861
 
3.9%
i 3615
 
3.7%
d 2645
 
2.7%
Other values (33) 22710
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14300
14.5%
a 11394
11.5%
o 10397
10.5%
S 8457
 
8.6%
n 8388
 
8.5%
s 7035
 
7.1%
5952
 
6.0%
r 3861
 
3.9%
i 3615
 
3.7%
d 2645
 
2.7%
Other values (33) 22710
23.0%

attributes_value_name
Text

Missing 

Distinct1372
Distinct (%)12.3%
Missing78850
Missing (%)87.6%
Memory size3.1 MiB
2025-02-21T23:10:15.372621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length29
Mean length8.4286996
Min length0

Characters and Unicode

Total characters93980
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1094 ?
Unique (%)9.8%

Sample

1st row37123
2nd rowSpring-Summer
3rd row2
4th rowSpring-Summer
5th rowAll-Season
ValueCountFrequency (%)
all-season 4013
37.8%
autumn-winter 2046
19.3%
spring-summer 843
 
7.9%
no 603
 
5.7%
256
 
2.4%
mujer 188
 
1.8%
hombre 111
 
1.0%
2015 48
 
0.5%
m2 45
 
0.4%
2014 43
 
0.4%
Other values (1388) 2432
22.9%
2025-02-21T23:10:15.943540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 8976
 
9.6%
l 8081
 
8.6%
e 7296
 
7.8%
- 6960
 
7.4%
A 6145
 
6.5%
S 6013
 
6.4%
u 5140
 
5.5%
o 4870
 
5.2%
t 4221
 
4.5%
a 4186
 
4.5%
Other values (63) 32092
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 8976
 
9.6%
l 8081
 
8.6%
e 7296
 
7.8%
- 6960
 
7.4%
A 6145
 
6.5%
S 6013
 
6.4%
u 5140
 
5.5%
o 4870
 
5.2%
t 4221
 
4.5%
a 4186
 
4.5%
Other values (63) 32092
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 8976
 
9.6%
l 8081
 
8.6%
e 7296
 
7.8%
- 6960
 
7.4%
A 6145
 
6.5%
S 6013
 
6.4%
u 5140
 
5.5%
o 4870
 
5.2%
t 4221
 
4.5%
a 4186
 
4.5%
Other values (63) 32092
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 8976
 
9.6%
l 8081
 
8.6%
e 7296
 
7.8%
- 6960
 
7.4%
A 6145
 
6.5%
S 6013
 
6.4%
u 5140
 
5.5%
o 4870
 
5.2%
t 4221
 
4.5%
a 4186
 
4.5%
Other values (63) 32092
34.1%

attributes_attribute_group_name
Categorical

High correlation  Imbalance  Missing 

Distinct7
Distinct (%)0.1%
Missing78850
Missing (%)87.6%
Memory size5.7 MiB
Ficha técnica
5755 
Otros
4448 
Sonido
838 
Ficha tecnica
 
76
Seguridad
 
21
Other values (2)
 
12

Length

Max length15
Median length13
Mean length9.2769507
Min length5

Characters and Unicode

Total characters103438
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOtros
2nd rowFicha técnica
3rd rowOtros
4th rowFicha técnica
5th rowFicha técnica

Common Values

ValueCountFrequency (%)
Ficha técnica 5755
 
6.4%
Otros 4448
 
4.9%
Sonido 838
 
0.9%
Ficha tecnica 76
 
0.1%
Seguridad 21
 
< 0.1%
Rango de Cuotas 11
 
< 0.1%
FICHA TECNICA 1
 
< 0.1%
(Missing) 78850
87.6%

Length

2025-02-21T23:10:16.070062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:16.195308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ficha 5832
34.3%
técnica 5755
33.8%
otros 4448
26.2%
sonido 838
 
4.9%
tecnica 77
 
0.5%
seguridad 21
 
0.1%
rango 11
 
0.1%
de 11
 
0.1%
cuotas 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
c 17493
16.9%
i 12521
12.1%
a 11705
11.3%
t 10290
9.9%
n 6680
 
6.5%
o 6146
 
5.9%
5854
 
5.7%
F 5832
 
5.6%
h 5831
 
5.6%
é 5755
 
5.6%
Other values (16) 15331
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 17493
16.9%
i 12521
12.1%
a 11705
11.3%
t 10290
9.9%
n 6680
 
6.5%
o 6146
 
5.9%
5854
 
5.7%
F 5832
 
5.6%
h 5831
 
5.6%
é 5755
 
5.6%
Other values (16) 15331
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 17493
16.9%
i 12521
12.1%
a 11705
11.3%
t 10290
9.9%
n 6680
 
6.5%
o 6146
 
5.9%
5854
 
5.7%
F 5832
 
5.6%
h 5831
 
5.6%
é 5755
 
5.6%
Other values (16) 15331
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 17493
16.9%
i 12521
12.1%
a 11705
11.3%
t 10290
9.9%
n 6680
 
6.5%
o 6146
 
5.9%
5854
 
5.7%
F 5832
 
5.6%
h 5831
 
5.6%
é 5755
 
5.6%
Other values (16) 15331
14.8%

attributes_id
Categorical

High correlation  Imbalance  Missing 

Distinct38
Distinct (%)0.3%
Missing78850
Missing (%)87.6%
Memory size5.5 MiB
Season
6918 
PART_NUMBER
1472 
MLA1744-USB
804 
GENDER
 
317
MLA1472-MTRSTOTAL
 
297
Other values (33)
1342 

Length

Max length19
Median length6
Mean length8.3453812
Min length4

Characters and Unicode

Total characters93051
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPART_NUMBER
2nd rowSeason
3rd rowMLA-PART_NUMBER
4th rowSeason
5th rowSeason

Common Values

ValueCountFrequency (%)
Season 6918
 
7.7%
PART_NUMBER 1472
 
1.6%
MLA1744-USB 804
 
0.9%
GENDER 317
 
0.4%
MLA1472-MTRSTOTAL 297
 
0.3%
MLA-PART_NUMBER 244
 
0.3%
MLA1466-MTRSTOTAL 240
 
0.3%
MLA1763-YEAR 168
 
0.2%
MLA1493-MTRSTOTAL 148
 
0.2%
MLA1744-YEAR 83
 
0.1%
Other values (28) 459
 
0.5%
(Missing) 78850
87.6%

Length

2025-02-21T23:10:16.387073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
season 6919
62.1%
part_number 1472
 
13.2%
mla1744-usb 804
 
7.2%
gender 317
 
2.8%
mla1472-mtrstotal 297
 
2.7%
mla-part_number 244
 
2.2%
mla1466-mtrstotal 240
 
2.2%
mla1763-year 168
 
1.5%
mla1493-mtrstotal 148
 
1.3%
mla1744-year 83
 
0.7%
Other values (27) 458
 
4.1%

Most occurring characters

ValueCountFrequency (%)
S 8510
 
9.1%
a 6918
 
7.4%
s 6918
 
7.4%
o 6918
 
7.4%
n 6918
 
7.4%
e 6918
 
7.4%
A 5107
 
5.5%
M 4925
 
5.3%
R 4881
 
5.2%
T 4106
 
4.4%
Other values (29) 30932
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93051
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 8510
 
9.1%
a 6918
 
7.4%
s 6918
 
7.4%
o 6918
 
7.4%
n 6918
 
7.4%
e 6918
 
7.4%
A 5107
 
5.5%
M 4925
 
5.3%
R 4881
 
5.2%
T 4106
 
4.4%
Other values (29) 30932
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93051
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 8510
 
9.1%
a 6918
 
7.4%
s 6918
 
7.4%
o 6918
 
7.4%
n 6918
 
7.4%
e 6918
 
7.4%
A 5107
 
5.5%
M 4925
 
5.3%
R 4881
 
5.2%
T 4106
 
4.4%
Other values (29) 30932
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93051
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 8510
 
9.1%
a 6918
 
7.4%
s 6918
 
7.4%
o 6918
 
7.4%
n 6918
 
7.4%
e 6918
 
7.4%
A 5107
 
5.5%
M 4925
 
5.3%
R 4881
 
5.2%
T 4106
 
4.4%
Other values (29) 30932
33.2%

buying_mode
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
buy_it_now
87311 
classified
 
1982
auction
 
707

Length

Max length10
Median length10
Mean length9.9764333
Min length7

Characters and Unicode

Total characters897879
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbuy_it_now
2nd rowbuy_it_now
3rd rowbuy_it_now
4th rowbuy_it_now
5th rowbuy_it_now

Common Values

ValueCountFrequency (%)
buy_it_now 87311
97.0%
classified 1982
 
2.2%
auction 707
 
0.8%

Length

2025-02-21T23:10:16.545680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:16.647595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
buy_it_now 87311
97.0%
classified 1982
 
2.2%
auction 707
 
0.8%

Most occurring characters

ValueCountFrequency (%)
_ 174622
19.4%
i 91982
10.2%
u 88018
9.8%
t 88018
9.8%
n 88018
9.8%
o 88018
9.8%
b 87311
9.7%
y 87311
9.7%
w 87311
9.7%
s 3964
 
0.4%
Other values (6) 13306
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 897879
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 174622
19.4%
i 91982
10.2%
u 88018
9.8%
t 88018
9.8%
n 88018
9.8%
o 88018
9.8%
b 87311
9.7%
y 87311
9.7%
w 87311
9.7%
s 3964
 
0.4%
Other values (6) 13306
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 897879
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 174622
19.4%
i 91982
10.2%
u 88018
9.8%
t 88018
9.8%
n 88018
9.8%
o 88018
9.8%
b 87311
9.7%
y 87311
9.7%
w 87311
9.7%
s 3964
 
0.4%
Other values (6) 13306
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 897879
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 174622
19.4%
i 91982
10.2%
u 88018
9.8%
t 88018
9.8%
n 88018
9.8%
o 88018
9.8%
b 87311
9.7%
y 87311
9.7%
w 87311
9.7%
s 3964
 
0.4%
Other values (6) 13306
 
1.5%

tags_0
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing22412
Missing (%)24.9%
Memory size6.5 MiB
dragged_bids_and_visits
65315 
good_quality_thumbnail
 
1537
dragged_visits
 
723
poor_quality_thumbnail
 
13

Length

Max length23
Median length23
Mean length22.880792
Min length14

Characters and Unicode

Total characters1546467
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdragged_bids_and_visits
2nd rowdragged_bids_and_visits
3rd rowdragged_bids_and_visits
4th rowdragged_bids_and_visits
5th rowdragged_bids_and_visits

Common Values

ValueCountFrequency (%)
dragged_bids_and_visits 65315
72.6%
good_quality_thumbnail 1537
 
1.7%
dragged_visits 723
 
0.8%
poor_quality_thumbnail 13
 
< 0.1%
(Missing) 22412
 
24.9%

Length

2025-02-21T23:10:16.792525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:16.902488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dragged_bids_and_visits 65315
96.6%
good_quality_thumbnail 1537
 
2.3%
dragged_visits 723
 
1.1%
poor_quality_thumbnail 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d 264243
17.1%
i 200491
13.0%
_ 199768
12.9%
s 197391
12.8%
a 134453
8.7%
g 133613
8.6%
t 69138
 
4.5%
b 66865
 
4.3%
n 66865
 
4.3%
r 66051
 
4.3%
Other values (10) 147589
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1546467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 264243
17.1%
i 200491
13.0%
_ 199768
12.9%
s 197391
12.8%
a 134453
8.7%
g 133613
8.6%
t 69138
 
4.5%
b 66865
 
4.3%
n 66865
 
4.3%
r 66051
 
4.3%
Other values (10) 147589
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1546467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 264243
17.1%
i 200491
13.0%
_ 199768
12.9%
s 197391
12.8%
a 134453
8.7%
g 133613
8.6%
t 69138
 
4.5%
b 66865
 
4.3%
n 66865
 
4.3%
r 66051
 
4.3%
Other values (10) 147589
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1546467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 264243
17.1%
i 200491
13.0%
_ 199768
12.9%
s 197391
12.8%
a 134453
8.7%
g 133613
8.6%
t 69138
 
4.5%
b 66865
 
4.3%
n 66865
 
4.3%
r 66051
 
4.3%
Other values (10) 147589
9.5%

tags
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB

tags_1
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.1%
Missing88540
Missing (%)98.4%
Memory size5.5 MiB
dragged_bids_and_visits
1201 
free_relist
259 

Length

Max length23
Median length23
Mean length20.871233
Min length11

Characters and Unicode

Total characters30472
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdragged_bids_and_visits
2nd rowdragged_bids_and_visits
3rd rowdragged_bids_and_visits
4th rowdragged_bids_and_visits
5th rowdragged_bids_and_visits

Common Values

ValueCountFrequency (%)
dragged_bids_and_visits 1201
 
1.3%
free_relist 259
 
0.3%
(Missing) 88540
98.4%

Length

2025-02-21T23:10:17.042061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:17.140832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dragged_bids_and_visits 1201
82.3%
free_relist 259
 
17.7%

Most occurring characters

ValueCountFrequency (%)
d 4804
15.8%
_ 3862
12.7%
i 3862
12.7%
s 3862
12.7%
a 2402
7.9%
g 2402
7.9%
e 1978
6.5%
r 1719
 
5.6%
t 1460
 
4.8%
b 1201
 
3.9%
Other values (4) 2920
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 4804
15.8%
_ 3862
12.7%
i 3862
12.7%
s 3862
12.7%
a 2402
7.9%
g 2402
7.9%
e 1978
6.5%
r 1719
 
5.6%
t 1460
 
4.8%
b 1201
 
3.9%
Other values (4) 2920
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 4804
15.8%
_ 3862
12.7%
i 3862
12.7%
s 3862
12.7%
a 2402
7.9%
g 2402
7.9%
e 1978
6.5%
r 1719
 
5.6%
t 1460
 
4.8%
b 1201
 
3.9%
Other values (4) 2920
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 4804
15.8%
_ 3862
12.7%
i 3862
12.7%
s 3862
12.7%
a 2402
7.9%
g 2402
7.9%
e 1978
6.5%
r 1719
 
5.6%
t 1460
 
4.8%
b 1201
 
3.9%
Other values (4) 2920
9.6%

listing_source
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
90000 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
90000
100.0%

Length

2025-02-21T23:10:17.259603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:17.332176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

parent_item_id
Text

Missing 

Distinct69310
Distinct (%)100.0%
Missing20690
Missing (%)23.0%
Memory size5.3 MiB
2025-02-21T23:10:17.587032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters901030
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69310 ?
Unique (%)100.0%

Sample

1st rowMLA6553902747
2nd rowMLA7727150374
3rd rowMLA6561247998
4th rowMLA3133256685
5th rowMLA5588379672
ValueCountFrequency (%)
mla6553902747 1
 
< 0.1%
mla9355243399 1
 
< 0.1%
mla2171981721 1
 
< 0.1%
mla3303447830 1
 
< 0.1%
mla2683610483 1
 
< 0.1%
mla6561247998 1
 
< 0.1%
mla3133256685 1
 
< 0.1%
mla5588379672 1
 
< 0.1%
mla8744215055 1
 
< 0.1%
mla4442923846 1
 
< 0.1%
Other values (69300) 69300
> 99.9%
2025-02-21T23:10:18.042778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 70483
 
7.8%
3 70383
 
7.8%
8 70358
 
7.8%
7 70135
 
7.8%
2 70034
 
7.8%
5 69997
 
7.8%
9 69989
 
7.8%
1 69734
 
7.7%
6 69657
 
7.7%
M 69310
 
7.7%
Other values (3) 200950
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 901030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 70483
 
7.8%
3 70383
 
7.8%
8 70358
 
7.8%
7 70135
 
7.8%
2 70034
 
7.8%
5 69997
 
7.8%
9 69989
 
7.8%
1 69734
 
7.7%
6 69657
 
7.7%
M 69310
 
7.7%
Other values (3) 200950
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 901030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 70483
 
7.8%
3 70383
 
7.8%
8 70358
 
7.8%
7 70135
 
7.8%
2 70034
 
7.8%
5 69997
 
7.8%
9 69989
 
7.8%
1 69734
 
7.7%
6 69657
 
7.7%
M 69310
 
7.7%
Other values (3) 200950
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 901030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 70483
 
7.8%
3 70383
 
7.8%
8 70358
 
7.8%
7 70135
 
7.8%
2 70034
 
7.8%
5 69997
 
7.8%
9 69989
 
7.8%
1 69734
 
7.7%
6 69657
 
7.7%
M 69310
 
7.7%
Other values (3) 200950
22.3%

coverage_areas
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB
Distinct10491
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
2025-02-21T23:10:18.317425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.8482
Min length7

Characters and Unicode

Total characters706338
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3797 ?
Unique (%)4.2%

Sample

1st rowMLA126406
2nd rowMLA10267
3rd rowMLA1227
4th rowMLA86345
5th rowMLA41287
ValueCountFrequency (%)
mla1227 4139
 
4.6%
mla2044 1759
 
2.0%
mla41287 829
 
0.9%
mla3530 685
 
0.8%
mla2038 601
 
0.7%
mla15171 522
 
0.6%
mla15328 446
 
0.5%
mla1383 416
 
0.5%
mla41269 399
 
0.4%
mla15204 395
 
0.4%
Other values (10481) 79809
88.7%
2025-02-21T23:10:18.776711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 90000
12.7%
L 90000
12.7%
A 90000
12.7%
1 64311
9.1%
2 55610
7.9%
3 52106
7.4%
4 47485
6.7%
7 43237
6.1%
0 39948
5.7%
5 36202
 
5.1%
Other values (3) 97439
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 706338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 90000
12.7%
L 90000
12.7%
A 90000
12.7%
1 64311
9.1%
2 55610
7.9%
3 52106
7.4%
4 47485
6.7%
7 43237
6.1%
0 39948
5.7%
5 36202
 
5.1%
Other values (3) 97439
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 706338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 90000
12.7%
L 90000
12.7%
A 90000
12.7%
1 64311
9.1%
2 55610
7.9%
3 52106
7.4%
4 47485
6.7%
7 43237
6.1%
0 39948
5.7%
5 36202
 
5.1%
Other values (3) 97439
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 706338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 90000
12.7%
L 90000
12.7%
A 90000
12.7%
1 64311
9.1%
2 55610
7.9%
3 52106
7.4%
4 47485
6.7%
7 43237
6.1%
0 39948
5.7%
5 36202
 
5.1%
Other values (3) 97439
13.8%

descriptions_0
Text

Missing 

Distinct87583
Distinct (%)100.0%
Missing2417
Missing (%)2.7%
Memory size7.6 MiB
2025-02-21T23:10:19.050011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters2890239
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87583 ?
Unique (%)100.0%

Sample

1st row{'id': 'MLA4695330653-912855983'}
2nd row{'id': 'MLA7160447179-930764806'}
3rd row{'id': 'MLA7367189936-916478256'}
4th row{'id': 'MLA9191625553-932309698'}
5th row{'id': 'MLA7787961817-902981678'}
ValueCountFrequency (%)
id 87583
50.0%
mla7263512597-931979681 1
 
< 0.1%
mla7787961817-902981678 1
 
< 0.1%
mla6542680143-907239038 1
 
< 0.1%
mla8224797916-939033580 1
 
< 0.1%
mla1395917367-944499723 1
 
< 0.1%
mla6054779321-934355208 1
 
< 0.1%
mla3801903502-936622752 1
 
< 0.1%
mla5838480559-918931162 1
 
< 0.1%
mla3458937254-896308868 1
 
< 0.1%
Other values (87574) 87574
50.0%
2025-02-21T23:10:19.478080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 350332
 
12.1%
9 236776
 
8.2%
3 171070
 
5.9%
2 167623
 
5.8%
1 165700
 
5.7%
4 162252
 
5.6%
8 156896
 
5.4%
0 155816
 
5.4%
7 150149
 
5.2%
6 149362
 
5.2%
Other values (11) 1024263
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2890239
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 350332
 
12.1%
9 236776
 
8.2%
3 171070
 
5.9%
2 167623
 
5.8%
1 165700
 
5.7%
4 162252
 
5.6%
8 156896
 
5.4%
0 155816
 
5.4%
7 150149
 
5.2%
6 149362
 
5.2%
Other values (11) 1024263
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2890239
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 350332
 
12.1%
9 236776
 
8.2%
3 171070
 
5.9%
2 167623
 
5.8%
1 165700
 
5.7%
4 162252
 
5.6%
8 156896
 
5.4%
0 155816
 
5.4%
7 150149
 
5.2%
6 149362
 
5.2%
Other values (11) 1024263
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2890239
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 350332
 
12.1%
9 236776
 
8.2%
3 171070
 
5.9%
2 167623
 
5.8%
1 165700
 
5.7%
4 162252
 
5.6%
8 156896
 
5.4%
0 155816
 
5.4%
7 150149
 
5.2%
6 149362
 
5.2%
Other values (11) 1024263
35.4%

descriptions
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB
Distinct86015
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size703.3 KiB
Minimum2014-11-25 04:08:06+00:00
Maximum2015-10-15 10:48:48.026000+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-21T23:10:19.630927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:10:19.831369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

international_delivery_mode
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
none
90000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters360000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rownone
3rd rownone
4th rownone
5th rownone

Common Values

ValueCountFrequency (%)
none 90000
100.0%

Length

2025-02-21T23:10:20.016420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:20.095288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
none 90000
100.0%

Most occurring characters

ValueCountFrequency (%)
n 180000
50.0%
o 90000
25.0%
e 90000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 180000
50.0%
o 90000
25.0%
e 90000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 180000
50.0%
o 90000
25.0%
e 90000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 180000
50.0%
o 90000
25.0%
e 90000
25.0%
Distinct7592
Distinct (%)8.5%
Missing703
Missing (%)0.8%
Memory size5.5 MiB
2025-02-21T23:10:20.449183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9839748
Min length3

Characters and Unicode

Total characters623648
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5034 ?
Unique (%)5.6%

Sample

1st row500x375
2nd row500x326
3rd row375x500
4th row500x372
5th row375x500
ValueCountFrequency (%)
500x375 20382
22.8%
375x500 10905
 
12.2%
500x500 5024
 
5.6%
500x281 2526
 
2.8%
281x500 1730
 
1.9%
500x333 1207
 
1.4%
500x374 986
 
1.1%
500x280 940
 
1.1%
400x400 899
 
1.0%
250x250 778
 
0.9%
Other values (7582) 43920
49.2%
2025-02-21T23:10:20.999515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 179735
28.8%
5 120867
19.4%
x 89297
14.3%
3 71027
 
11.4%
7 43755
 
7.0%
2 32652
 
5.2%
4 25611
 
4.1%
1 21463
 
3.4%
8 17270
 
2.8%
9 12385
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 623648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 179735
28.8%
5 120867
19.4%
x 89297
14.3%
3 71027
 
11.4%
7 43755
 
7.0%
2 32652
 
5.2%
4 25611
 
4.1%
1 21463
 
3.4%
8 17270
 
2.8%
9 12385
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 623648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 179735
28.8%
5 120867
19.4%
x 89297
14.3%
3 71027
 
11.4%
7 43755
 
7.0%
2 32652
 
5.2%
4 25611
 
4.1%
1 21463
 
3.4%
8 17270
 
2.8%
9 12385
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 623648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 179735
28.8%
5 120867
19.4%
x 89297
14.3%
3 71027
 
11.4%
7 43755
 
7.0%
2 32652
 
5.2%
4 25611
 
4.1%
1 21463
 
3.4%
8 17270
 
2.8%
9 12385
 
2.0%
Distinct89276
Distinct (%)> 99.9%
Missing703
Missing (%)0.8%
Memory size11.7 MiB
https://www.mercadolibre.com/jm/img?s=STC&v=O&f=proccesing_image_es.jpg
 
18
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/6279-MLApp_27_21489919_1-O.jpg
 
4
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/6270-MLApp_27_19602074_1-O.jpg
 
2
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/5361-MLA4695330653_052013-O.jpg
 
1
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/511801-MLA5521774469_092015-O.jpg
 
1
Other values (89271)
89271 
(Missing)
 
703
ValueCountFrequency (%)
https://www.mercadolibre.com/jm/img?s=STC&v=O&f=proccesing_image_es.jpg 18
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/6279-MLApp_27_21489919_1-O.jpg 4
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/6270-MLApp_27_19602074_1-O.jpg 2
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/5361-MLA4695330653_052013-O.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/511801-MLA5521774469_092015-O.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/10415-MLA9275558126_012014-O.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/488901-MLA7676511257_092015-O.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/18241-MLA7045024947_082014-O.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/435901-MLA4897289669_092015-O.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/472701-MLA6685721284_082015-O.jpg 1
 
< 0.1%
Other values (89266) 89266
99.2%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
https 89297
99.2%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
a248.e.akamai.net 89279
99.2%
www.mercadolibre.com 18
 
< 0.1%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
/jm/img 18
 
< 0.1%
/mla-s1-p.mlstatic.com/6279-MLApp_27_21489919_1-O.jpg 4
 
< 0.1%
/mla-s1-p.mlstatic.com/6270-MLApp_27_19602074_1-O.jpg 2
 
< 0.1%
/mla-s1-p.mlstatic.com/4859-MLA8666780654_032013-O.jpg 1
 
< 0.1%
/mla-s1-p.mlstatic.com/996901-MLA5845364612_102015-O.jpg 1
 
< 0.1%
/mla-s2-p.mlstatic.com/10415-MLA9275558126_012014-O.jpg 1
 
< 0.1%
/mla-s2-p.mlstatic.com/488901-MLA7676511257_092015-O.jpg 1
 
< 0.1%
/mla-s1-p.mlstatic.com/18241-MLA7045024947_082014-O.jpg 1
 
< 0.1%
/mla-s1-p.mlstatic.com/435901-MLA4897289669_092015-O.jpg 1
 
< 0.1%
/mla-s1-p.mlstatic.com/472701-MLA6685721284_082015-O.jpg 1
 
< 0.1%
Other values (89266) 89266
99.2%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
89279
99.2%
s=STC&v=O&f=proccesing_image_es.jpg 18
 
< 0.1%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
89297
99.2%
(Missing) 703
 
0.8%
Distinct17475
Distinct (%)19.6%
Missing703
Missing (%)0.8%
Memory size5.5 MiB
2025-02-21T23:10:21.338925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.5591789
Min length0

Characters and Unicode

Total characters675012
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12732 ?
Unique (%)14.3%

Sample

1st row1200x900
2nd row924x603
3rd row900x1200
4th row500x372
5th row480x640
ValueCountFrequency (%)
1200x900 14716
 
16.5%
900x1200 8303
 
9.3%
640x480 2247
 
2.5%
1200x675 2026
 
2.3%
675x1200 1437
 
1.6%
500x500 1297
 
1.5%
480x640 1180
 
1.3%
1024x768 936
 
1.0%
400x400 899
 
1.0%
1200x1200 876
 
1.0%
Other values (17464) 55310
62.0%
2025-02-21T23:10:21.830070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 214182
31.7%
x 89227
13.2%
2 73932
 
11.0%
1 72300
 
10.7%
9 43972
 
6.5%
4 34838
 
5.2%
5 33872
 
5.0%
6 32263
 
4.8%
8 28574
 
4.2%
7 28477
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 675012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 214182
31.7%
x 89227
13.2%
2 73932
 
11.0%
1 72300
 
10.7%
9 43972
 
6.5%
4 34838
 
5.2%
5 33872
 
5.0%
6 32263
 
4.8%
8 28574
 
4.2%
7 28477
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 675012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 214182
31.7%
x 89227
13.2%
2 73932
 
11.0%
1 72300
 
10.7%
9 43972
 
6.5%
4 34838
 
5.2%
5 33872
 
5.0%
6 32263
 
4.8%
8 28574
 
4.2%
7 28477
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 675012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 214182
31.7%
x 89227
13.2%
2 73932
 
11.0%
1 72300
 
10.7%
9 43972
 
6.5%
4 34838
 
5.2%
5 33872
 
5.0%
6 32263
 
4.8%
8 28574
 
4.2%
7 28477
 
4.2%
Distinct89276
Distinct (%)> 99.9%
Missing703
Missing (%)0.8%
Memory size10.1 MiB
http://www.mercadolibre.com/jm/img?s=STC&v=O&f=proccesing_image_es.jpg
 
18
http://mla-s1-p.mlstatic.com/6279-MLApp_27_21489919_1-O.jpg
 
4
http://mla-s1-p.mlstatic.com/6270-MLApp_27_19602074_1-O.jpg
 
2
http://mla-s1-p.mlstatic.com/5361-MLA4695330653_052013-O.jpg
 
1
http://mla-s1-p.mlstatic.com/511801-MLA5521774469_092015-O.jpg
 
1
Other values (89271)
89271 
(Missing)
 
703
ValueCountFrequency (%)
http://www.mercadolibre.com/jm/img?s=STC&v=O&f=proccesing_image_es.jpg 18
 
< 0.1%
http://mla-s1-p.mlstatic.com/6279-MLApp_27_21489919_1-O.jpg 4
 
< 0.1%
http://mla-s1-p.mlstatic.com/6270-MLApp_27_19602074_1-O.jpg 2
 
< 0.1%
http://mla-s1-p.mlstatic.com/5361-MLA4695330653_052013-O.jpg 1
 
< 0.1%
http://mla-s1-p.mlstatic.com/511801-MLA5521774469_092015-O.jpg 1
 
< 0.1%
http://mla-s2-p.mlstatic.com/10415-MLA9275558126_012014-O.jpg 1
 
< 0.1%
http://mla-s2-p.mlstatic.com/488901-MLA7676511257_092015-O.jpg 1
 
< 0.1%
http://mla-s1-p.mlstatic.com/18241-MLA7045024947_082014-O.jpg 1
 
< 0.1%
http://mla-s1-p.mlstatic.com/435901-MLA4897289669_092015-O.jpg 1
 
< 0.1%
http://mla-s1-p.mlstatic.com/472701-MLA6685721284_082015-O.jpg 1
 
< 0.1%
Other values (89266) 89266
99.2%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
http 89297
99.2%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
mla-s2-p.mlstatic.com 44694
49.7%
mla-s1-p.mlstatic.com 44585
49.5%
www.mercadolibre.com 18
 
< 0.1%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
/jm/img 18
 
< 0.1%
/6279-MLApp_27_21489919_1-O.jpg 4
 
< 0.1%
/6270-MLApp_27_19602074_1-O.jpg 2
 
< 0.1%
/4859-MLA8666780654_032013-O.jpg 1
 
< 0.1%
/996901-MLA5845364612_102015-O.jpg 1
 
< 0.1%
/10415-MLA9275558126_012014-O.jpg 1
 
< 0.1%
/488901-MLA7676511257_092015-O.jpg 1
 
< 0.1%
/18241-MLA7045024947_082014-O.jpg 1
 
< 0.1%
/435901-MLA4897289669_092015-O.jpg 1
 
< 0.1%
/472701-MLA6685721284_082015-O.jpg 1
 
< 0.1%
Other values (89266) 89266
99.2%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
89279
99.2%
s=STC&v=O&f=proccesing_image_es.jpg 18
 
< 0.1%
(Missing) 703
 
0.8%
ValueCountFrequency (%)
89297
99.2%
(Missing) 703
 
0.8%

pictures_quality
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing703
Missing (%)0.8%
Memory size4.9 MiB
89297 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
89297
99.2%
(Missing) 703
 
0.8%

Length

2025-02-21T23:10:21.983198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:22.054322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct89293
Distinct (%)> 99.9%
Missing703
Missing (%)0.8%
Memory size7.1 MiB
2025-02-21T23:10:22.379473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length27
Mean length26.157866
Min length20

Characters and Unicode

Total characters2335819
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89291 ?
Unique (%)> 99.9%

Sample

1st row5361-MLA4695330653_052013
2nd row23201-MLA7160447179_022015
3rd row22076-MLA7367189936_012015
4th row632901-MLA9191625553_092015
5th row13596-MLA7787961817_1837
ValueCountFrequency (%)
6279-mlapp_27_21489919_1 4
 
< 0.1%
6270-mlapp_27_19602074_1 2
 
< 0.1%
616701-mla7449546633_082015 1
 
< 0.1%
13230-mla5070808455_042014 1
 
< 0.1%
9809-mla1395917367_122013 1
 
< 0.1%
124901-mla6054779321_092015 1
 
< 0.1%
22826-mla3801903502_022015 1
 
< 0.1%
11353-mla6309248837_022014 1
 
< 0.1%
14068-mla3458937254_042014 1
 
< 0.1%
19922-mla7263512597_102014 1
 
< 0.1%
Other values (89283) 89283
> 99.9%
2025-02-21T23:10:22.893529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 315307
13.5%
1 301071
12.9%
2 230735
9.9%
5 184601
7.9%
4 156345
 
6.7%
3 144971
 
6.2%
9 144023
 
6.2%
8 138401
 
5.9%
6 137974
 
5.9%
7 135198
 
5.8%
Other values (6) 447193
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2335819
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 315307
13.5%
1 301071
12.9%
2 230735
9.9%
5 184601
7.9%
4 156345
 
6.7%
3 144971
 
6.2%
9 144023
 
6.2%
8 138401
 
5.9%
6 137974
 
5.9%
7 135198
 
5.8%
Other values (6) 447193
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2335819
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 315307
13.5%
1 301071
12.9%
2 230735
9.9%
5 184601
7.9%
4 156345
 
6.7%
3 144971
 
6.2%
9 144023
 
6.2%
8 138401
 
5.9%
6 137974
 
5.9%
7 135198
 
5.8%
Other values (6) 447193
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2335819
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 315307
13.5%
1 301071
12.9%
2 230735
9.9%
5 184601
7.9%
4 156345
 
6.7%
3 144971
 
6.2%
9 144023
 
6.2%
8 138401
 
5.9%
6 137974
 
5.9%
7 135198
 
5.8%
Other values (6) 447193
19.1%

pictures
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB

official_store_id
Real number (ℝ)

High correlation  Missing 

Distinct193
Distinct (%)25.9%
Missing89255
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean5802.0188
Minimum1024
Maximum9996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:23.054018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1024
5-th percentile1580
Q13590
median6158
Q38216
95-th percentile9639.6
Maximum9996
Range8972
Interquartile range (IQR)4626

Descriptive statistics

Standard deviation2657.0563
Coefficient of variation (CV)0.45795376
Kurtosis-1.1569147
Mean5802.0188
Median Absolute Deviation (MAD)2400
Skewness-0.14974349
Sum4322504
Variance7059948.2
MonotonicityNot monotonic
2025-02-21T23:10:23.247076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1580 37
 
< 0.1%
6448 27
 
< 0.1%
6732 23
 
< 0.1%
4701 21
 
< 0.1%
9218 20
 
< 0.1%
5192 18
 
< 0.1%
4273 17
 
< 0.1%
7208 16
 
< 0.1%
9317 16
 
< 0.1%
9189 15
 
< 0.1%
Other values (183) 535
 
0.6%
(Missing) 89255
99.2%
ValueCountFrequency (%)
1024 1
 
< 0.1%
1044 1
 
< 0.1%
1046 1
 
< 0.1%
1056 3
 
< 0.1%
1123 1
 
< 0.1%
1245 1
 
< 0.1%
1315 4
 
< 0.1%
1330 10
< 0.1%
1350 11
< 0.1%
1530 1
 
< 0.1%
ValueCountFrequency (%)
9996 1
 
< 0.1%
9971 4
 
< 0.1%
9934 4
 
< 0.1%
9933 1
 
< 0.1%
9911 1
 
< 0.1%
9906 7
< 0.1%
9875 10
< 0.1%
9729 4
 
< 0.1%
9720 1
 
< 0.1%
9703 1
 
< 0.1%

differential_pricing
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB

accepts_mercadopago
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size88.0 KiB
True
88018 
False
 
1982
ValueCountFrequency (%)
True 88018
97.8%
False 1982
 
2.2%
2025-02-21T23:10:23.394449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

original_price
Real number (ℝ)

High correlation  Missing 

Distinct105
Distinct (%)80.8%
Missing89870
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean1492.5223
Minimum120
Maximum12248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:23.601927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile194.5
Q1449
median850
Q31500
95-th percentile4504.95
Maximum12248
Range12128
Interquartile range (IQR)1051

Descriptive statistics

Standard deviation1954.9057
Coefficient of variation (CV)1.3098
Kurtosis15.561427
Mean1492.5223
Median Absolute Deviation (MAD)451
Skewness3.5682009
Sum194027.9
Variance3821656.5
MonotonicityNot monotonic
2025-02-21T23:10:23.866310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 3
 
< 0.1%
449 3
 
< 0.1%
750 3
 
< 0.1%
350 3
 
< 0.1%
850 3
 
< 0.1%
1900 3
 
< 0.1%
599 3
 
< 0.1%
450 2
 
< 0.1%
299 2
 
< 0.1%
2899 2
 
< 0.1%
Other values (95) 103
 
0.1%
(Missing) 89870
99.9%
ValueCountFrequency (%)
120 2
< 0.1%
149 1
< 0.1%
150 1
< 0.1%
159.9 1
< 0.1%
170 1
< 0.1%
190 1
< 0.1%
200 1
< 0.1%
250 1
< 0.1%
262 1
< 0.1%
296 1
< 0.1%
ValueCountFrequency (%)
12248 1
< 0.1%
11999 1
< 0.1%
10610 1
< 0.1%
5499 1
< 0.1%
5350 1
< 0.1%
4810 1
< 0.1%
4599 1
< 0.1%
4390 1
< 0.1%
4130 1
< 0.1%
3899 1
< 0.1%

currency_id
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
ARS
89496 
USD
 
504

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters270000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARS
2nd rowARS
3rd rowARS
4th rowARS
5th rowARS

Common Values

ValueCountFrequency (%)
ARS 89496
99.4%
USD 504
 
0.6%

Length

2025-02-21T23:10:24.214125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:24.394464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ars 89496
99.4%
usd 504
 
0.6%

Most occurring characters

ValueCountFrequency (%)
S 90000
33.3%
A 89496
33.1%
R 89496
33.1%
U 504
 
0.2%
D 504
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 270000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 90000
33.3%
A 89496
33.1%
R 89496
33.1%
U 504
 
0.2%
D 504
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 270000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 90000
33.3%
A 89496
33.1%
R 89496
33.1%
U 504
 
0.2%
D 504
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 270000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 90000
33.3%
A 89496
33.1%
R 89496
33.1%
U 504
 
0.2%
D 504
 
0.2%
Distinct89260
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size10.1 MiB
2025-02-21T23:10:24.783374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length70
Median length62
Mean length60.701322
Min length0

Characters and Unicode

Total characters5463119
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89256 ?
Unique (%)99.2%

Sample

1st rowhttp://mla-s1-p.mlstatic.com/5386-MLA4695330653_052013-I.jpg
2nd rowhttp://mla-s1-p.mlstatic.com/23223-MLA7160447179_022015-I.jpg
3rd rowhttp://mla-s1-p.mlstatic.com/22076-MLA7367189936_012015-I.jpg
4th rowhttp://mla-s2-p.mlstatic.com/183901-MLA9191625553_092015-I.jpg
5th rowhttp://mla-s2-p.mlstatic.com/13595-MLA7787961817_1713-I.jpg
ValueCountFrequency (%)
http://www.mercadolibre.com/jm/img?s=stc&v=i&f=proccesing_image_es.jpg 35
 
< 0.1%
http://mla-s1-p.mlstatic.com/6279-mlapp_27_21489919_1-i.jpg 4
 
< 0.1%
http://mla-s1-p.mlstatic.com/6270-mlapp_27_19602074_1-i.jpg 2
 
< 0.1%
http://mla-s2-p.mlstatic.com/15074-mla5070808455_052014-i.jpg 1
 
< 0.1%
http://mla-s2-p.mlstatic.com/9874-mla1395917367_122013-i.jpg 1
 
< 0.1%
http://mla-s1-p.mlstatic.com/797901-mla6054779321_092015-i.jpg 1
 
< 0.1%
http://mla-s1-p.mlstatic.com/22802-mla3801903502_022015-i.jpg 1
 
< 0.1%
http://mla-s1-p.mlstatic.com/11353-mla6309248837_022014-i.jpg 1
 
< 0.1%
http://mla-s2-p.mlstatic.com/14032-mla3458937254_042014-i.jpg 1
 
< 0.1%
http://mla-s2-p.mlstatic.com/19922-mla7263512597_102014-i.jpg 1
 
< 0.1%
Other values (89249) 89249
99.9%
2025-02-21T23:10:25.453784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 357118
 
6.5%
- 357048
 
6.5%
1 345876
 
6.3%
0 316119
 
5.8%
2 275063
 
5.0%
p 268259
 
4.9%
m 267961
 
4.9%
/ 267926
 
4.9%
. 267891
 
4.9%
5 185109
 
3.4%
Other values (35) 2554749
46.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5463119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 357118
 
6.5%
- 357048
 
6.5%
1 345876
 
6.3%
0 316119
 
5.8%
2 275063
 
5.0%
p 268259
 
4.9%
m 267961
 
4.9%
/ 267926
 
4.9%
. 267891
 
4.9%
5 185109
 
3.4%
Other values (35) 2554749
46.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5463119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 357118
 
6.5%
- 357048
 
6.5%
1 345876
 
6.3%
0 316119
 
5.8%
2 275063
 
5.0%
p 268259
 
4.9%
m 267961
 
4.9%
/ 267926
 
4.9%
. 267891
 
4.9%
5 185109
 
3.4%
Other values (35) 2554749
46.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5463119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 357118
 
6.5%
- 357048
 
6.5%
1 345876
 
6.3%
0 316119
 
5.8%
2 275063
 
5.0%
p 268259
 
4.9%
m 267961
 
4.9%
/ 267926
 
4.9%
. 267891
 
4.9%
5 185109
 
3.4%
Other values (35) 2554749
46.8%

title
Text

Distinct89008
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
2025-02-21T23:10:25.991567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length100
Median length82
Mean length45.319633
Min length3

Characters and Unicode

Total characters4078767
Distinct characters157
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88446 ?
Unique (%)98.3%

Sample

1st rowAuriculares Samsung Originales Manos Libres Cable Usb Oferta
2nd rowCuchillo Daga Acero Carbón Casco Yelmo Solingen Con Vaina
3rd rowAntigua Revista Billiken, N° 1826, Año 1954
4th rowAlarma Guardtex Gx412 Seguridad Para El Automotor!!!
5th rowSerenata - Jennifer Blake
ValueCountFrequency (%)
30835
 
4.6%
de 29478
 
4.4%
y 7675
 
1.2%
la 6407
 
1.0%
para 5414
 
0.8%
en 4892
 
0.7%
el 4814
 
0.7%
con 4632
 
0.7%
a 3062
 
0.5%
original 3001
 
0.5%
Other values (90034) 563022
84.9%
2025-02-21T23:10:26.966843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
587983
 
14.4%
a 354678
 
8.7%
e 293851
 
7.2%
o 273325
 
6.7%
r 218661
 
5.4%
i 216237
 
5.3%
n 174319
 
4.3%
l 157051
 
3.9%
s 155548
 
3.8%
t 145731
 
3.6%
Other values (147) 1501383
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4078767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
587983
 
14.4%
a 354678
 
8.7%
e 293851
 
7.2%
o 273325
 
6.7%
r 218661
 
5.4%
i 216237
 
5.3%
n 174319
 
4.3%
l 157051
 
3.9%
s 155548
 
3.8%
t 145731
 
3.6%
Other values (147) 1501383
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4078767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
587983
 
14.4%
a 354678
 
8.7%
e 293851
 
7.2%
o 273325
 
6.7%
r 218661
 
5.4%
i 216237
 
5.3%
n 174319
 
4.3%
l 157051
 
3.9%
s 155548
 
3.8%
t 145731
 
3.6%
Other values (147) 1501383
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4078767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
587983
 
14.4%
a 354678
 
8.7%
e 293851
 
7.2%
o 273325
 
6.7%
r 218661
 
5.4%
i 216237
 
5.3%
n 174319
 
4.3%
l 157051
 
3.9%
s 155548
 
3.8%
t 145731
 
3.6%
Other values (147) 1501383
36.8%

automatic_relist
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size88.0 KiB
False
85773 
True
 
4227
ValueCountFrequency (%)
False 85773
95.3%
True 4227
 
4.7%
2025-02-21T23:10:27.092583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct79247
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Memory size703.3 KiB
Minimum2013-05-21 04:22:35+00:00
Maximum2015-10-15 09:14:30+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-21T23:10:27.296712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:10:27.579403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct89260
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
2025-02-21T23:10:27.938577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length81
Median length81
Mean length79.545911
Min length0

Characters and Unicode

Total characters7159132
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89256 ?
Unique (%)99.2%

Sample

1st rowhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/5386-MLA4695330653_052013-I.jpg
2nd rowhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/23223-MLA7160447179_022015-I.jpg
3rd rowhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/22076-MLA7367189936_012015-I.jpg
4th rowhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/183901-MLA9191625553_092015-I.jpg
5th rowhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/13595-MLA7787961817_1713-I.jpg
ValueCountFrequency (%)
https://www.mercadolibre.com/jm/img?s=stc&v=i&f=proccesing_image_es.jpg 35
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/6279-mlapp_27_21489919_1-i.jpg 4
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/6270-mlapp_27_19602074_1-i.jpg 2
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/15074-mla5070808455_052014-i.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/9874-mla1395917367_122013-i.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/797901-mla6054779321_092015-i.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/22802-mla3801903502_022015-i.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/11353-mla6309248837_022014-i.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/14032-mla3458937254_042014-i.jpg 1
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/19922-mla7263512597_102014-i.jpg 1
 
< 0.1%
Other values (89249) 89249
99.9%
2025-02-21T23:10:28.403369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 535677
 
7.5%
a 535642
 
7.5%
t 446380
 
6.2%
2 364325
 
5.1%
m 357223
 
5.0%
/ 357188
 
5.0%
- 357048
 
5.0%
1 345876
 
4.8%
0 316119
 
4.4%
p 268259
 
3.7%
Other values (36) 3275395
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7159132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 535677
 
7.5%
a 535642
 
7.5%
t 446380
 
6.2%
2 364325
 
5.1%
m 357223
 
5.0%
/ 357188
 
5.0%
- 357048
 
5.0%
1 345876
 
4.8%
0 316119
 
4.4%
p 268259
 
3.7%
Other values (36) 3275395
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7159132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 535677
 
7.5%
a 535642
 
7.5%
t 446380
 
6.2%
2 364325
 
5.1%
m 357223
 
5.0%
/ 357188
 
5.0%
- 357048
 
5.0%
1 345876
 
4.8%
0 316119
 
4.4%
p 268259
 
3.7%
Other values (36) 3275395
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7159132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 535677
 
7.5%
a 535642
 
7.5%
t 446380
 
6.2%
2 364325
 
5.1%
m 357223
 
5.0%
/ 357188
 
5.0%
- 357048
 
5.0%
1 345876
 
4.8%
0 316119
 
4.4%
p 268259
 
3.7%
Other values (36) 3275395
45.8%

stop_time
Real number (ℝ)

High correlation  Skewed 

Distinct78486
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4476479 × 1012
Minimum1.4448235 × 1012
Maximum1.7307322 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:28.517980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.4448235 × 1012
5-th percentile1.445177 × 1012
Q11.4463133 × 1012
median1.4476429 × 1012
Q31.4489303 × 1012
95-th percentile1.4498843 × 1012
Maximum1.7307322 × 1012
Range2.8590865 × 1011
Interquartile range (IQR)2.6170902 × 109

Descriptive statistics

Standard deviation3.2510395 × 109
Coefficient of variation (CV)0.0022457391
Kurtosis5747.6951
Mean1.4476479 × 1012
Median Absolute Deviation (MAD)1.3209665 × 109
Skewness66.192269
Sum1.3028831 × 1017
Variance1.0569258 × 1019
MonotonicityNot monotonic
2025-02-21T23:10:28.706577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.446584296 × 101219
 
< 0.1%
1.44685878 × 101217
 
< 0.1%
1.447009249 × 101217
 
< 0.1%
1.447384665 × 101215
 
< 0.1%
1.446325154 × 101213
 
< 0.1%
1.445633821 × 101213
 
< 0.1%
1.446512336 × 101212
 
< 0.1%
1.445288232 × 101211
 
< 0.1%
1.446598616 × 101211
 
< 0.1%
1.446512236 × 101211
 
< 0.1%
Other values (78476) 89861
99.8%
ValueCountFrequency (%)
1.444823497 × 10121
< 0.1%
1.444823874 × 10121
< 0.1%
1.444832708 × 10121
< 0.1%
1.444848882 × 10121
< 0.1%
1.44485677 × 10121
< 0.1%
1.444865337 × 10121
< 0.1%
1.4448654 × 10121
< 0.1%
1.444870288 × 10121
< 0.1%
1.444876849 × 10121
< 0.1%
1.444877193 × 10121
< 0.1%
ValueCountFrequency (%)
1.730732151 × 10129
< 0.1%
1.47637166 × 10121
 
< 0.1%
1.476305804 × 10121
 
< 0.1%
1.476236899 × 10121
 
< 0.1%
1.475788501 × 10121
 
< 0.1%
1.475553646 × 10121
 
< 0.1%
1.47543222 × 10121
 
< 0.1%
1.475264589 × 10121
 
< 0.1%
1.474736496 × 10121
 
< 0.1%
1.474648216 × 10121
 
< 0.1%

status
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 MiB
active
86116 
paused
 
3863
closed
 
20
not_yet_active
 
1

Length

Max length14
Median length6
Mean length6.0000889
Min length6

Characters and Unicode

Total characters540008
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowactive
2nd rowactive
3rd rowactive
4th rowactive
5th rowactive

Common Values

ValueCountFrequency (%)
active 86116
95.7%
paused 3863
 
4.3%
closed 20
 
< 0.1%
not_yet_active 1
 
< 0.1%

Length

2025-02-21T23:10:28.884506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:28.989952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
active 86116
95.7%
paused 3863
 
4.3%
closed 20
 
< 0.1%
not_yet_active 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 90001
16.7%
a 89980
16.7%
c 86137
16.0%
t 86119
15.9%
i 86117
15.9%
v 86117
15.9%
s 3883
 
0.7%
d 3883
 
0.7%
p 3863
 
0.7%
u 3863
 
0.7%
Other values (5) 45
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 90001
16.7%
a 89980
16.7%
c 86137
16.0%
t 86119
15.9%
i 86117
15.9%
v 86117
15.9%
s 3883
 
0.7%
d 3883
 
0.7%
p 3863
 
0.7%
u 3863
 
0.7%
Other values (5) 45
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 90001
16.7%
a 89980
16.7%
c 86137
16.0%
t 86119
15.9%
i 86117
15.9%
v 86117
15.9%
s 3883
 
0.7%
d 3883
 
0.7%
p 3863
 
0.7%
u 3863
 
0.7%
Other values (5) 45
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 90001
16.7%
a 89980
16.7%
c 86137
16.0%
t 86119
15.9%
i 86117
15.9%
v 86117
15.9%
s 3883
 
0.7%
d 3883
 
0.7%
p 3863
 
0.7%
u 3863
 
0.7%
Other values (5) 45
 
< 0.1%

video_id
Text

Missing 

Distinct1886
Distinct (%)70.5%
Missing87324
Missing (%)97.0%
Memory size2.8 MiB
2025-02-21T23:10:29.245465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters29436
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1746 ?
Unique (%)65.2%

Sample

1st rowQQNfOicE_o8
2nd rowupGjgXun-lU
3rd rowYDvSXmSHKKE
4th rowQQNfOicE_o8
5th rowFyEJiX4Ufak
ValueCountFrequency (%)
qqnfoice_o8 278
 
10.4%
evcquwl7rie 82
 
3.1%
mynrc5ia1sk 35
 
1.3%
u7okrylubno 25
 
0.9%
6jhmxwttjoa 25
 
0.9%
orxwqovjvxg 23
 
0.9%
t0xbm8fb1eg 21
 
0.8%
77i4p9vfzak 13
 
0.5%
7xgwqauqhgy 12
 
0.4%
4xi4h2w1w-e 12
 
0.4%
Other values (1875) 2150
80.3%
2025-02-21T23:10:29.659538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Q 1126
 
3.8%
E 939
 
3.2%
o 850
 
2.9%
8 778
 
2.6%
c 770
 
2.6%
i 676
 
2.3%
N 646
 
2.2%
f 623
 
2.1%
w 619
 
2.1%
O 609
 
2.1%
Other values (54) 21800
74.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 1126
 
3.8%
E 939
 
3.2%
o 850
 
2.9%
8 778
 
2.6%
c 770
 
2.6%
i 676
 
2.3%
N 646
 
2.2%
f 623
 
2.1%
w 619
 
2.1%
O 609
 
2.1%
Other values (54) 21800
74.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 1126
 
3.8%
E 939
 
3.2%
o 850
 
2.9%
8 778
 
2.6%
c 770
 
2.6%
i 676
 
2.3%
N 646
 
2.2%
f 623
 
2.1%
w 619
 
2.1%
O 609
 
2.1%
Other values (54) 21800
74.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 1126
 
3.8%
E 939
 
3.2%
o 850
 
2.9%
8 778
 
2.6%
c 770
 
2.6%
i 676
 
2.3%
N 646
 
2.2%
f 623
 
2.1%
w 619
 
2.1%
O 609
 
2.1%
Other values (54) 21800
74.1%

catalog_product_id
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)100.0%
Missing89993
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2895699.7
Minimum94404
Maximum5126117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:29.765247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum94404
5-th percentile306306
Q11925517.5
median3051112
Q34073615
95-th percentile5116251.5
Maximum5126117
Range5031713
Interquartile range (IQR)2148097.5

Descriptive statistics

Standard deviation1919770.7
Coefficient of variation (CV)0.66297298
Kurtosis-0.97102776
Mean2895699.7
Median Absolute Deviation (MAD)2042120
Skewness-0.31189249
Sum20269898
Variance3.6855194 × 1012
MonotonicityNot monotonic
2025-02-21T23:10:29.889787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
800744 1
 
< 0.1%
5093232 1
 
< 0.1%
3050291 1
 
< 0.1%
5126117 1
 
< 0.1%
94404 1
 
< 0.1%
3051112 1
 
< 0.1%
3053998 1
 
< 0.1%
(Missing) 89993
> 99.9%
ValueCountFrequency (%)
94404 1
< 0.1%
800744 1
< 0.1%
3050291 1
< 0.1%
3051112 1
< 0.1%
3053998 1
< 0.1%
5093232 1
< 0.1%
5126117 1
< 0.1%
ValueCountFrequency (%)
5126117 1
< 0.1%
5093232 1
< 0.1%
3053998 1
< 0.1%
3051112 1
< 0.1%
3050291 1
< 0.1%
800744 1
< 0.1%
94404 1
< 0.1%

subtitle
Unsupported

Missing  Rejected  Unsupported 

Missing90000
Missing (%)100.0%
Memory size703.3 KiB

initial_quantity
Real number (ℝ)

High correlation  Skewed 

Distinct425
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.957178
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:30.061492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile44
Maximum9999
Range9998
Interquartile range (IQR)1

Descriptive statistics

Standard deviation421.09198
Coefficient of variation (CV)12.045938
Kurtosis505.30591
Mean34.957178
Median Absolute Deviation (MAD)0
Skewness21.856862
Sum3146146
Variance177318.46
MonotonicityNot monotonic
2025-02-21T23:10:30.263550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 63245
70.3%
2 4575
 
5.1%
10 4215
 
4.7%
3 3104
 
3.4%
5 2226
 
2.5%
4 1770
 
2.0%
20 954
 
1.1%
6 944
 
1.0%
100 923
 
1.0%
9 663
 
0.7%
Other values (415) 7381
 
8.2%
ValueCountFrequency (%)
1 63245
70.3%
2 4575
 
5.1%
3 3104
 
3.4%
4 1770
 
2.0%
5 2226
 
2.5%
6 944
 
1.0%
7 504
 
0.6%
8 585
 
0.7%
9 663
 
0.7%
10 4215
 
4.7%
ValueCountFrequency (%)
9999 95
0.1%
9998 8
 
< 0.1%
9997 7
 
< 0.1%
9996 2
 
< 0.1%
9995 2
 
< 0.1%
9992 2
 
< 0.1%
9988 2
 
< 0.1%
9987 1
 
< 0.1%
9984 1
 
< 0.1%
9977 4
 
< 0.1%

start_time
Real number (ℝ)

High correlation 

Distinct78890
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4423831 × 1012
Minimum1.3691102 × 1012
Maximum1.4449005 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:30.478590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.3691102 × 1012
5-th percentile1.4399474 × 1012
Q11.441144 × 1012
median1.4425239 × 1012
Q31.4437997 × 1012
95-th percentile1.4447243 × 1012
Maximum1.4449005 × 1012
Range7.5790315 × 1010
Interquartile range (IQR)2.6557092 × 109

Descriptive statistics

Standard deviation2.1226826 × 109
Coefficient of variation (CV)0.0014716497
Kurtosis186.2696
Mean1.4423831 × 1012
Median Absolute Deviation (MAD)1.305707 × 109
Skewness-8.6121038
Sum1.2981448 × 1017
Variance4.5057812 × 1018
MonotonicityNot monotonic
2025-02-21T23:10:30.691201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.442514419 × 10127
 
< 0.1%
1.441716432 × 10127
 
< 0.1%
1.443449439 × 10127
 
< 0.1%
1.441311725 × 10127
 
< 0.1%
1.444689463 × 10127
 
< 0.1%
1.440534507 × 10126
 
< 0.1%
1.440247668 × 10126
 
< 0.1%
1.442937502 × 10126
 
< 0.1%
1.441033805 × 10126
 
< 0.1%
1.442590946 × 10126
 
< 0.1%
Other values (78880) 89935
99.9%
ValueCountFrequency (%)
1.369110155 × 10121
< 0.1%
1.369147816 × 10121
< 0.1%
1.373581442 × 10121
< 0.1%
1.375146505 × 10121
< 0.1%
1.375884494 × 10121
< 0.1%
1.378585259 × 10121
< 0.1%
1.379104944 × 10121
< 0.1%
1.382037802 × 10121
< 0.1%
1.389366791 × 10121
< 0.1%
1.393426925 × 10121
< 0.1%
ValueCountFrequency (%)
1.44490047 × 10121
< 0.1%
1.444900237 × 10121
< 0.1%
1.444898886 × 10121
< 0.1%
1.444897049 × 10121
< 0.1%
1.444896956 × 10121
< 0.1%
1.444896942 × 10121
< 0.1%
1.444896654 × 10121
< 0.1%
1.44489663 × 10121
< 0.1%
1.44489653 × 10121
< 0.1%
1.444896346 × 10121
< 0.1%

sold_quantity
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct304
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3280444
Minimum0
Maximum6065
Zeros74834
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:30.898907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum6065
Range6065
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33.839328
Coefficient of variation (CV)14.535516
Kurtosis12789.767
Mean2.3280444
Median Absolute Deviation (MAD)0
Skewness88.254449
Sum209524
Variance1145.1001
MonotonicityNot monotonic
2025-02-21T23:10:31.095656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74834
83.1%
1 5924
 
6.6%
2 2246
 
2.5%
3 1196
 
1.3%
4 853
 
0.9%
5 596
 
0.7%
6 453
 
0.5%
7 352
 
0.4%
8 269
 
0.3%
9 250
 
0.3%
Other values (294) 3027
 
3.4%
ValueCountFrequency (%)
0 74834
83.1%
1 5924
 
6.6%
2 2246
 
2.5%
3 1196
 
1.3%
4 853
 
0.9%
5 596
 
0.7%
6 453
 
0.5%
7 352
 
0.4%
8 269
 
0.3%
9 250
 
0.3%
ValueCountFrequency (%)
6065 1
< 0.1%
2606 1
< 0.1%
2299 1
< 0.1%
2175 1
< 0.1%
2088 1
< 0.1%
2032 1
< 0.1%
1540 1
< 0.1%
1373 1
< 0.1%
1367 1
< 0.1%
1116 1
< 0.1%

available_quantity
Real number (ℝ)

High correlation  Skewed 

Distinct441
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.700767
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size703.3 KiB
2025-02-21T23:10:31.298419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile42
Maximum9999
Range9998
Interquartile range (IQR)1

Descriptive statistics

Standard deviation420.8117
Coefficient of variation (CV)12.12687
Kurtosis505.83792
Mean34.700767
Median Absolute Deviation (MAD)0
Skewness21.872042
Sum3123069
Variance177082.49
MonotonicityNot monotonic
2025-02-21T23:10:31.522086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 63732
70.8%
2 4737
 
5.3%
10 3886
 
4.3%
3 3184
 
3.5%
5 2052
 
2.3%
4 1801
 
2.0%
6 894
 
1.0%
100 847
 
0.9%
20 841
 
0.9%
9 749
 
0.8%
Other values (431) 7277
 
8.1%
ValueCountFrequency (%)
1 63732
70.8%
2 4737
 
5.3%
3 3184
 
3.5%
4 1801
 
2.0%
5 2052
 
2.3%
6 894
 
1.0%
7 523
 
0.6%
8 626
 
0.7%
9 749
 
0.8%
10 3886
 
4.3%
ValueCountFrequency (%)
9999 66
0.1%
9998 16
 
< 0.1%
9997 12
 
< 0.1%
9996 6
 
< 0.1%
9995 2
 
< 0.1%
9994 2
 
< 0.1%
9993 1
 
< 0.1%
9992 1
 
< 0.1%
9991 1
 
< 0.1%
9989 1
 
< 0.1%

0
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
new
48352 
used
41648 

Length

Max length4
Median length3
Mean length3.4627556
Min length3

Characters and Unicode

Total characters311648
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownew
2nd rowused
3rd rowused
4th rownew
5th rowused

Common Values

ValueCountFrequency (%)
new 48352
53.7%
used 41648
46.3%

Length

2025-02-21T23:10:31.691360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-21T23:10:31.786015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new 48352
53.7%
used 41648
46.3%

Most occurring characters

ValueCountFrequency (%)
e 90000
28.9%
n 48352
15.5%
w 48352
15.5%
u 41648
13.4%
s 41648
13.4%
d 41648
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 311648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 90000
28.9%
n 48352
15.5%
w 48352
15.5%
u 41648
13.4%
s 41648
13.4%
d 41648
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 311648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 90000
28.9%
n 48352
15.5%
w 48352
15.5%
u 41648
13.4%
s 41648
13.4%
d 41648
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 311648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 90000
28.9%
n 48352
15.5%
w 48352
15.5%
u 41648
13.4%
s 41648
13.4%
d 41648
13.4%

Interactions

2025-02-21T23:09:52.040757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:12.054534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:16.048348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:18.499434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:20.902373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:23.221327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:25.600542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:29.100369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:34.167848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:36.342904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:38.382992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:40.935711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:44.308300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:47.072186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:49.562967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:52.223836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:12.348171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:16.246035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:18.654716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:21.055950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:23.401181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:25.749231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:29.398471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:34.325665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:36.471110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:38.546761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:41.137053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:44.537725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:47.264082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:49.739845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:52.389059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:12.605366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:16.402874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:18.809501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:21.223080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:23.549464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:25.893186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:29.653555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:34.471098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:36.603887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:38.701022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:41.363958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:44.770386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:47.422555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:49.893811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:52.546514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:12.897962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:16.561995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:18.964866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:21.403652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:23.740655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:26.094059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:29.906841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:34.598931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:36.737228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:38.872648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:41.556714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:45.031818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:47.600734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:50.060798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:52.702501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:13.178665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:16.716971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:19.123154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:21.561388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:23.895060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:26.328399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:30.136385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:34.735697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:36.882521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:39.035590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:41.779556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:45.304612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:47.746516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:50.247406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:52.862306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:13.432913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:16.870191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:19.330127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:21.716592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:24.062061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:26.597550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:30.378663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:34.875818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:37.024475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:39.203136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:42.014559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:45.514495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:47.893914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:50.418315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:53.025995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:13.693150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:17.033318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:19.493834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:21.875756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:24.218769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:26.761271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:30.656136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:35.018077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:37.167903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:39.408063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:42.254600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:45.677223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:48.058388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:50.597124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:53.174339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:13.959243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:17.194720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:19.652461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:22.035215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:24.386240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:27.039837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:30.825622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:35.170931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:37.322247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:39.555615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:42.521917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:45.818750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:48.240580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:50.756274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:53.321857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:14.206602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:17.365260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:19.783464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:22.154149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:24.535778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:27.330723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:30.983177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:35.327864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:37.448080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:39.703940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:42.738734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:45.954190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:48.377472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:50.905406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:53.463615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:14.482559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:17.506990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:19.922185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:22.335529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:24.671272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:27.603882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:31.131016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:35.447603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:37.577104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:39.845521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:42.965170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:46.091281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:48.516391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:51.040000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:53.619098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:14.754838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:17.663302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:20.086216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:22.491105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:24.824073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:27.877423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:31.307091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:35.603398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:37.710794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:39.993736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:43.225392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:46.277586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:48.688073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:51.208968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:53.757998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:15.016959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:17.795361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:20.232824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:22.605923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:24.937751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:28.051590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:31.436728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:35.716906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:37.821135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:40.149355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:43.395937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:46.422946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:48.823270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:51.360082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:53.921471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:15.310179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:17.959847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:20.411715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:22.753825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:25.091799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:28.289298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:31.600949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:35.860137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:37.950828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:40.343797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:43.592455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:46.591560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:48.989303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:51.517263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:54.096592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:15.533229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:18.133228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:20.574176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:22.901973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:25.246788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:28.540965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:31.777836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:36.024371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:38.084917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:40.518901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:43.800888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:46.758506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:49.168867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:51.697286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:54.296558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:15.799284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:18.344337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:20.744104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:23.064692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:25.420278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:28.825651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:34.013958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:36.187509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:38.226364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:40.716414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:44.048024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:46.914679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:49.388000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-21T23:09:51.877223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-21T23:10:34.486244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
0accepts_mercadopagoattributes_attribute_group_idattributes_attribute_group_nameattributes_idattributes_nameautomatic_relistavailable_quantitybase_pricebuying_modecatalog_product_idconditioncurrency_iddeal_ids_0initial_quantitylisting_type_idnon_mercado_pago_payment_methods_descriptionnon_mercado_pago_payment_methods_idnon_mercado_pago_payment_methods_typeofficial_store_idoriginal_pricepriceseller_address_country.nameseller_address_state.nameseller_idshipping_free_shippingshipping_local_pick_upshipping_modesold_quantitystart_timestatusstop_timesub_status_0tags_0tags_1variations_available_quantityvariations_idvariations_pricevariations_sold_quantity
01.0000.0920.3510.3510.4710.4650.1860.0390.0040.1010.4241.0000.0250.7270.0390.5120.1130.1130.0560.3391.0000.0040.0000.0610.0520.1370.0420.1000.0140.0200.0610.0100.0000.1230.7800.0050.0380.0000.028
accepts_mercadopago0.0921.0000.6690.6920.9980.9990.0330.0000.0311.0001.0000.0920.5000.9330.0000.4010.0290.0290.0001.0001.0000.0310.0000.0950.0180.0260.2960.1650.0000.4130.0100.1040.2091.0000.9981.0001.0001.0001.000
attributes_attribute_group_id0.3510.6691.0001.0000.9210.9220.2390.0250.0120.4730.0000.3510.2000.6940.0250.2670.0480.0480.0000.3150.2250.0121.0000.0850.0290.0940.4660.1991.0000.1220.0250.0230.3260.4720.7540.0000.0241.0000.000
attributes_attribute_group_name0.3510.6921.0001.0000.7810.7620.2390.0180.0000.4890.0000.3510.2570.6940.0180.2270.0270.0270.0000.3150.2250.0001.0000.0710.0290.0940.4790.2061.0000.2080.0230.0310.3260.4870.7540.0000.3531.0000.000
attributes_id0.4710.9980.9210.7811.0000.9960.4060.0000.2840.7050.0000.4710.7590.6060.0000.3620.2000.2000.0000.1660.6240.2841.0000.0880.0700.1540.6550.3201.0000.2380.0000.1640.2660.7170.9660.0000.0001.0000.048
attributes_name0.4650.9990.9220.7620.9961.0000.4070.0320.2860.7060.0000.4650.7540.6060.0320.3580.2010.2010.0000.1660.6240.2861.0000.0830.0630.1590.6560.3141.0000.1980.0250.1390.2660.7190.9760.0000.0031.0000.073
automatic_relist0.1860.0330.2390.2390.4060.4071.0000.0400.0000.0390.0000.1860.0160.5670.0410.7940.0630.0630.0510.3250.1990.0000.0000.0740.0260.1470.0030.1650.0260.0100.0350.0000.1520.1090.2160.0000.0210.0000.000
available_quantity0.0390.0000.0250.0180.0000.0320.0401.0000.0570.0000.6120.0390.0000.1470.9910.0460.0050.0050.015-0.1120.2610.0570.0000.000-0.0130.0400.0110.0080.310-0.0040.0000.0161.0000.0350.0000.656-0.0080.0150.134
base_price0.0040.0310.0120.0000.2840.2860.0000.0571.0000.022-0.8570.0040.0001.0000.0600.0070.0000.0000.0000.0940.9831.0000.0000.058-0.0090.0000.0080.0000.0470.0240.0100.0311.0000.0520.000-0.0280.0221.0000.027
buying_mode0.1011.0000.4730.4890.7050.7060.0390.0000.0221.0001.0000.1010.5000.9330.0000.2840.0220.0220.0001.0001.0000.0220.0000.0710.0340.0300.2960.1340.0000.2920.0720.0740.6050.7080.9981.0001.0001.0001.000
catalog_product_id0.4241.0000.0000.0000.0000.0000.0000.612-0.8571.0001.0000.4241.0000.0000.6120.5481.0001.0001.000NaNNaN-0.8571.0000.0000.4641.0001.0000.6120.6120.1430.0000.1430.0001.0000.000NaNNaNNaNNaN
condition1.0000.0920.3510.3510.4710.4650.1860.0390.0040.1010.4241.0000.0250.7270.0390.5120.1130.1130.0560.3391.0000.0040.0000.0610.0520.1370.0420.1000.0140.0200.0610.0100.0000.1230.7800.0050.0380.0000.028
currency_id0.0250.5000.2000.2570.7590.7540.0160.0000.0000.5001.0000.0251.0001.0000.0000.2101.0001.0001.0001.0001.0000.0000.0000.0410.0170.0120.1480.0820.0000.3470.0100.1031.0000.4760.4671.0001.0001.0001.000
deal_ids_00.7270.9330.6940.6940.6060.6060.5670.1471.0000.9330.0000.7271.0001.0000.1470.0000.1760.1760.5370.1770.1621.0001.0000.4060.2330.1800.6070.4111.0000.0280.0001.0000.0000.6961.0001.0000.2121.0001.000
initial_quantity0.0390.0000.0250.0180.0000.0320.0410.9910.0600.0000.6120.0390.0000.1471.0000.0460.0000.0000.015-0.1140.2800.0600.0000.000-0.0120.0410.0110.0070.340-0.0100.0000.0111.0000.0650.0160.636-0.0120.0190.173
listing_type_id0.5120.4010.2670.2270.3620.3580.7940.0460.0070.2840.5480.5120.2100.0000.0461.0000.0470.0470.0570.0340.2450.0070.0000.0550.0250.2060.0940.1380.0480.1050.0340.0940.3270.2930.4430.0000.0380.0000.084
non_mercado_pago_payment_methods_description0.1130.0290.0480.0270.2000.2010.0630.0050.0000.0221.0000.1131.0000.1760.0000.0471.0001.0001.0000.4800.0000.0001.0000.0730.0750.1320.2260.0670.0160.0070.0191.0000.1780.0181.0000.0000.0001.0000.040
non_mercado_pago_payment_methods_id0.1130.0290.0480.0270.2000.2010.0630.0050.0000.0221.0000.1131.0000.1760.0000.0471.0001.0001.0000.4800.0000.0001.0000.0730.0750.1320.2260.0670.0160.0070.0191.0000.1780.0181.0000.0000.0001.0000.040
non_mercado_pago_payment_methods_type0.0560.0000.0000.0000.0000.0000.0510.0150.0000.0001.0000.0561.0000.5370.0150.0571.0001.0001.0000.5270.0000.0001.0000.0700.0640.0030.1390.0450.0290.0000.0211.0000.0000.0001.0000.0000.0001.0000.000
official_store_id0.3391.0000.3150.3150.1660.1660.325-0.1120.0941.000NaN0.3391.0000.177-0.1140.0340.4800.4800.5271.000-0.3510.0941.0000.1250.0860.0930.3740.2920.0260.0760.1610.0761.0000.1071.0000.0360.0880.073-0.037
original_price1.0001.0000.2250.2250.6240.6240.1990.2610.9831.000NaN1.0001.0000.1620.2800.2450.0000.0000.000-0.3511.0000.9831.0000.0000.0390.2930.1600.3980.140-0.0200.0000.0270.0000.3591.0000.195-0.0200.9760.107
price0.0040.0310.0120.0000.2840.2860.0000.0571.0000.022-0.8570.0040.0001.0000.0600.0070.0000.0000.0000.0940.9831.0000.0000.058-0.0090.0000.0080.0000.0470.0250.0100.0311.0000.0520.000-0.0280.0221.0000.027
seller_address_country.name0.0000.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.0000.0001.0000.0000.0001.0001.0001.0001.0001.0000.0001.0001.0000.0020.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0001.0001.0001.000
seller_address_state.name0.0610.0950.0850.0710.0880.0830.0740.0000.0580.0710.0000.0610.0410.4060.0000.0550.0730.0730.0700.1250.0000.0581.0001.0000.0550.0640.1280.0570.0000.0040.0200.0000.0890.0440.2620.0000.0280.0000.000
seller_id0.0520.0180.0290.0290.0700.0630.026-0.013-0.0090.0340.4640.0520.0170.233-0.0120.0250.0750.0750.0640.0860.039-0.0090.0020.0551.0000.0820.0550.0430.006-0.0050.022-0.0030.1100.0130.000-0.0540.006-0.001-0.004
shipping_free_shipping0.1370.0260.0940.0940.1540.1590.1470.0400.0000.0301.0000.1370.0120.1800.0410.2060.1320.1320.0030.0930.2930.0000.0000.0640.0821.0000.0230.1710.0000.0030.0170.0000.0000.0970.1530.0000.0000.0000.028
shipping_local_pick_up0.0420.2960.4660.4790.6550.6560.0030.0110.0080.2961.0000.0420.1480.6070.0110.0940.2260.2260.1390.3740.1600.0080.0000.1280.0550.0231.0000.1610.0030.1220.0090.0300.1110.2070.7670.0280.0550.0000.000
shipping_mode0.1000.1650.1990.2060.3200.3140.1650.0080.0000.1340.6120.1000.0820.4110.0070.1380.0670.0670.0450.2920.3980.0000.0000.0570.0430.1710.1611.0000.0040.0370.0170.0110.0440.0680.4710.0000.0240.0000.000
sold_quantity0.0140.0001.0001.0001.0001.0000.0260.3100.0470.0000.6120.0140.0001.0000.3400.0480.0160.0160.0290.0260.1400.0470.0000.0000.0060.0000.0030.0041.000-0.0040.0000.0141.0000.0380.0060.081-0.0140.0740.552
start_time0.0200.4130.1220.2080.2380.1980.010-0.0040.0240.2920.1430.0200.3470.028-0.0100.1050.0070.0070.0000.076-0.0200.0250.0000.004-0.0050.0030.1220.037-0.0041.0000.0120.9391.0000.2150.0330.0160.9900.017-0.010
status0.0610.0100.0250.0230.0000.0250.0350.0000.0100.0720.0000.0610.0100.0000.0000.0340.0190.0190.0210.1610.0000.0100.0000.0200.0220.0170.0090.0170.0000.0121.0000.0000.9990.0130.0260.0000.0400.0000.000
stop_time0.0100.1040.0230.0310.1640.1390.0000.0160.0310.0740.1430.0100.1031.0000.0110.0941.0001.0001.0000.0760.0270.0310.0000.000-0.0030.0000.0300.0110.0140.9390.0001.0001.0000.0901.0000.0130.9800.018-0.009
sub_status_00.0000.2090.3260.3260.2660.2660.1521.0001.0000.6050.0000.0001.0000.0001.0000.3270.1780.1780.0001.0000.0001.0001.0000.0890.1100.0000.1110.0441.0001.0000.9991.0001.0000.0001.0001.0000.1621.0001.000
tags_00.1231.0000.4720.4870.7170.7190.1090.0350.0520.7081.0000.1230.4760.6960.0650.2930.0180.0180.0000.1070.3590.0520.0000.0440.0130.0970.2070.0680.0380.2150.0130.0900.0001.0001.0000.0000.0001.0000.129
tags_10.7800.9980.7540.7540.9660.9760.2160.0000.0000.9980.0000.7800.4671.0000.0160.4431.0001.0001.0001.0001.0000.0001.0000.2620.0000.1530.7670.4710.0060.0330.0261.0001.0001.0001.0001.0001.0001.0001.000
variations_available_quantity0.0051.0000.0000.0000.0000.0000.0000.656-0.0281.000NaN0.0051.0001.0000.6360.0000.0000.0000.0000.0360.195-0.0281.0000.000-0.0540.0000.0280.0000.0810.0160.0000.0131.0000.0001.0001.0000.012-0.0280.105
variations_id0.0381.0000.0240.3530.0000.0030.021-0.0080.0221.000NaN0.0381.0000.212-0.0120.0380.0000.0000.0000.088-0.0200.0221.0000.0280.0060.0000.0550.024-0.0140.9900.0400.9800.1620.0001.0000.0121.0000.022-0.010
variations_price0.0001.0001.0001.0001.0001.0000.0000.0151.0001.000NaN0.0001.0001.0000.0190.0001.0001.0001.0000.0730.9761.0001.0000.000-0.0010.0000.0000.0000.0740.0170.0000.0181.0001.0001.000-0.0280.0221.0000.027
variations_sold_quantity0.0281.0000.0000.0000.0480.0730.0000.1340.0271.000NaN0.0281.0001.0000.1730.0840.0400.0400.000-0.0370.1070.0271.0000.000-0.0040.0280.0000.0000.552-0.0100.000-0.0091.0000.1291.0000.105-0.0100.0271.000

Missing values

2025-02-21T23:09:55.347759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-21T23:09:57.670191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-21T23:10:01.112018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

seller_address_country.nameseller_address_state.nameseller_address_city.namewarrantysub_statussub_status_0conditiondeal_idsdeal_ids_0base_priceshipping_local_pick_upshipping_methodsshipping_tagsshipping_free_shippingshipping_modeshipping_dimensionsshipping_free_methodsnon_mercado_pago_payment_methods_descriptionnon_mercado_pago_payment_methods_idnon_mercado_pago_payment_methods_typenon_mercado_pago_payment_methodsseller_idvariationsvariations_attribute_combinationsvariations_seller_custom_fieldvariations_picture_idsvariations_sold_quantityvariations_available_quantityvariations_idvariations_pricesite_idlisting_type_idpriceattributesattributes_value_idattributes_attribute_group_idattributes_nameattributes_value_nameattributes_attribute_group_nameattributes_idbuying_modetags_0tagstags_1listing_sourceparent_item_idcoverage_areascategory_iddescriptions_0descriptionslast_updatedinternational_delivery_modepictures_sizepictures_secure_urlpictures_max_sizepictures_urlpictures_qualitypictures_idpicturesofficial_store_iddifferential_pricingaccepts_mercadopagooriginal_pricecurrency_idthumbnailtitleautomatic_relistdate_createdsecure_thumbnailstop_timestatusvideo_idcatalog_product_idsubtitleinitial_quantitystart_timesold_quantityavailable_quantity0
0ArgentinaCapital FederalSan Cristóbal<NA>NaNNaNnewNaNNaN80.0True[][]Falsenot_specifiedNoneNaNEfectivoMLAMOGNaN8208882349NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze80.0NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA6553902747NaNMLA126406{'id': 'MLA4695330653-912855983'}NaN2015-09-05T20:42:58.000Znone500x375https://a248.e.akamai.net/mla-s1-p.mlstatic.com/5361-MLA4695330653_052013-O.jpg1200x900http://mla-s1-p.mlstatic.com/5361-MLA4695330653_052013-O.jpg5361-MLA4695330653_052013NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/5386-MLA4695330653_052013-I.jpgAuriculares Samsung Originales Manos Libres Cable Usb OfertaFalse2015-09-05T20:42:53.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/5386-MLA4695330653_052013-I.jpg1446669773000activeNoneNaNNaT1144148577300001new
1ArgentinaCapital FederalBuenos AiresNUESTRA REPUTACIONNaNNaNusedNaNNaN2650.0True[][]Falseme2NoneNaNEfectivoMLAMOGNaN8141699488NaNNaNNaNNaNNaNNaNNaNNaNMLAsilver2650.0NaNNaNNaNNaNNaNNaNNaNbuy_it_nowNaNNaNNaNMLA7727150374NaNMLA10267{'id': 'MLA7160447179-930764806'}NaN2015-09-26T18:08:34.000Znone500x326https://a248.e.akamai.net/mla-s1-p.mlstatic.com/23201-MLA7160447179_022015-O.jpg924x603http://mla-s1-p.mlstatic.com/23201-MLA7160447179_022015-O.jpg23201-MLA7160447179_022015NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/23223-MLA7160447179_022015-I.jpgCuchillo Daga Acero Carbón Casco Yelmo Solingen Con VainaFalse2015-09-26T18:08:30.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/23223-MLA7160447179_022015-I.jpg1448474910000activeNoneNaNNaT1144329091000001used
2ArgentinaCapital FederalBoedo<NA>NaNNaNusedNaNNaN60.0True[][]Falseme2NoneNaNEfectivoMLAMOGNaN8386096505NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze60.0NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA6561247998NaNMLA1227{'id': 'MLA7367189936-916478256'}NaN2015-09-09T23:57:10.000Znone375x500https://a248.e.akamai.net/mla-s1-p.mlstatic.com/22076-MLA7367189936_012015-O.jpg900x1200http://mla-s1-p.mlstatic.com/22076-MLA7367189936_012015-O.jpg22076-MLA7367189936_012015NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/22076-MLA7367189936_012015-I.jpgAntigua Revista Billiken, N° 1826, Año 1954False2015-09-09T23:57:07.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/22076-MLA7367189936_012015-I.jpg1447027027000activeNoneNaNNaT1144184302700001used
3ArgentinaCapital FederalFloresta<NA>NaNNaNnewNaNNaN580.0True[][]Falseme2NoneNaNEfectivoMLAMOGNaN5377752182NaNNaNNaNNaNNaNNaNNaNNaNMLAsilver580.0NaNNaNNaNNaNNaNNaNNaNbuy_it_nowNaNNaNNaNNoneNaNMLA86345{'id': 'MLA9191625553-932309698'}NaN2015-10-05T16:03:50.306Znone500x372https://a248.e.akamai.net/mla-s2-p.mlstatic.com/632901-MLA9191625553_092015-O.jpg500x372http://mla-s2-p.mlstatic.com/632901-MLA9191625553_092015-O.jpg632901-MLA9191625553_092015NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/183901-MLA9191625553_092015-I.jpgAlarma Guardtex Gx412 Seguridad Para El Automotor!!!False2015-09-28T18:47:56.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/183901-MLA9191625553_092015-I.jpg1449191596000activeNoneNaNNaT1144346607600001new
4ArgentinaBuenos AiresTres de febreroMI REPUTACION.NaNNaNusedNaNNaN30.0True[][]Falsenot_specifiedNoneNaNEfectivoMLAMOGNaN2938071313NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze30.0NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA3133256685NaNMLA41287{'id': 'MLA7787961817-902981678'}NaN2015-08-28T13:37:41.000Znone375x500https://a248.e.akamai.net/mla-s1-p.mlstatic.com/13596-MLA7787961817_1837-O.jpg480x640http://mla-s1-p.mlstatic.com/13596-MLA7787961817_1837-O.jpg13596-MLA7787961817_1837NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/13595-MLA7787961817_1713-I.jpgSerenata - Jennifer BlakeFalse2015-08-24T22:07:20.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/13595-MLA7787961817_1713-I.jpg1445638040000activeNoneNaNNaT1144045404000001used
5ArgentinaBuenos AiresVilla AdelinaNaNNaNnewNaNNaN310.0True[][]Falsenot_specifiedNoneNaNEfectivoMLAMOGNaN4086336590NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze310.0NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA5588379672NaNMLA1429{'id': 'MLA6542680143-907239038'}NaN2015-08-30T14:24:09.000Znone250x250https://a248.e.akamai.net/mla-s2-p.mlstatic.com/6207-MLA6542680143_3765-O.jpg250x250http://mla-s2-p.mlstatic.com/6207-MLA6542680143_3765-O.jpg6207-MLA6542680143_3765NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/6207-MLA6542680143_3765-I.jpgClavo De Olor(*1/2) Grano Origen TurquiaFalse2015-08-30T14:24:02.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/6207-MLA6542680143_3765-I.jpg1446128641000activeNoneNaNNaT1144094464100001new
6ArgentinaCapital FederalBarracas<NA>NaNNaNusedNaNNaN180.0True[][]Falseme2NoneNaNNaNNaNNaNNaN9645655791NaN[{'value_id': '92012', 'name': 'Color Primario', 'value_name': 'Azul petróleo', 'id': '83000'}, {'value_id': '82034', 'name': 'Color Secundario', 'value_name': 'Amarillo', 'id': '73001'}, {'value_id': '141996', 'name': 'Talle', 'value_name': '8', 'id': '103000'}]None[472901-MLA20442937232_102015, 509801-MLA20442939057_102015, 650901-MLA20442940047_102015, 373901-MLA20442951026_102015, 422901-MLA20442950410_102015]0.01.09.742953e+09180.0MLAfree180.0NaNNaNNaNNaNNaNNaNNaNbuy_it_nowNaNNaNNaNNoneNaNMLA352650{'id': 'MLA8224797916-939033580'}NaN2015-10-06T22:32:46.000Znone500x375https://a248.e.akamai.net/mla-s2-p.mlstatic.com/422901-MLA8224797916_102015-O.jpg1200x900http://mla-s2-p.mlstatic.com/422901-MLA8224797916_102015-O.jpg422901-MLA8224797916_102015NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/472901-MLA8224797916_102015-I.jpgShort Nike Fit Boca Juniors Talle S 7-8 AñosFalse2015-10-06T22:32:41.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/472901-MLA8224797916_102015-I.jpg1449354761000activeNoneNaNNaT1144417076100001used
7ArgentinaBuenos AiresAvellaneda/Capital Federal<NA>NaNNaNusedNaNNaN150.0False[][]Falsenot_specifiedNoneNaNEfectivoMLAMOGNaN4755818264NaNNaNNaNNaNNaNNaNNaNNaNMLAfree150.0NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA8744215055NaNMLA1227{'id': 'MLA1395917367-944499723'}NaN2015-10-14T19:36:54.000Znone500x375https://a248.e.akamai.net/mla-s1-p.mlstatic.com/9809-MLA1395917367_122013-O.jpg1024x768http://mla-s1-p.mlstatic.com/9809-MLA1395917367_122013-O.jpg9809-MLA1395917367_122013NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/9874-MLA1395917367_122013-I.jpgPlaza De Toros Iglesias Vistas Aereas De España 1965False2015-10-14T19:36:52.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/9874-MLA1395917367_122013-I.jpg1450035412000activeNoneNaNNaT1144485141200001used
8ArgentinaCapital FederalCapital Federal1 AnoNaNNaNnewNaNNaN2352.0TrueNaN[]Trueme2None[{'rule': {'value': None, 'free_mode': 'country'}, 'id': 73328}]EfectivoMLAMOGNaN1809776792NaNNaNNaNNaNNaNNaNNaNNaNMLAgold_special2352.0NaNDFLTNúmero de pieza37123OtrosPART_NUMBERbuy_it_nowNaNNaNNaNNoneNaNMLA352293{'id': 'MLA6054779321-934355208'}NaN2015-10-02T00:01:26.000Znone500x500https://a248.e.akamai.net/mla-s2-p.mlstatic.com/124901-MLA6054779321_092015-O.jpg650x650http://mla-s2-p.mlstatic.com/124901-MLA6054779321_092015-O.jpg124901-MLA6054779321_092015NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/797901-MLA6054779321_092015-I.jpgKit X2 Amortiguador Trasero Monroe Chrysler Caravan 01True2015-09-30T21:50:22.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/797901-MLA6054779321_092015-I.jpg1448833822000pausedQQNfOicE_o8NaNNaT101443649822000010new
9ArgentinaBuenos Airesmonte grande<NA>NaNNaNnewNaNNaN120.0True[][]Falseme2NoneNaNEfectivoMLAMOGNaN2852894385NaN[{'value_id': '92021', 'name': 'Color Primario', 'value_name': 'Fucsia', 'id': '83000'}, {'value_id': '141992', 'name': 'Talle', 'value_name': '1', 'id': '103000'}]None[22802-MLA20237759642_022015, 22826-MLA20237759652_022015]1.01.09.720380e+09120.0MLAsilver120.0NaNSeason-Spring-SummerFINDSeasonSpring-SummerFicha técnicaSeasonbuy_it_nowNaNNaNNaNMLA4442923846NaNMLA121665{'id': 'MLA3801903502-936622752'}NaN2015-10-08T20:02:01.000Znone375x500https://a248.e.akamai.net/mla-s2-p.mlstatic.com/22826-MLA3801903502_022015-O.jpg480x640http://mla-s2-p.mlstatic.com/22826-MLA3801903502_022015-O.jpg22826-MLA3801903502_022015NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/22802-MLA3801903502_022015-I.jpgBonitas Mallas Color Fuccia Talle 1 Y 2False2015-10-03T23:11:30.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/22802-MLA3801903502_022015-I.jpg1449097889000activeNoneNaNNaT2144391388900011new
seller_address_country.nameseller_address_state.nameseller_address_city.namewarrantysub_statussub_status_0conditiondeal_idsdeal_ids_0base_priceshipping_local_pick_upshipping_methodsshipping_tagsshipping_free_shippingshipping_modeshipping_dimensionsshipping_free_methodsnon_mercado_pago_payment_methods_descriptionnon_mercado_pago_payment_methods_idnon_mercado_pago_payment_methods_typenon_mercado_pago_payment_methodsseller_idvariationsvariations_attribute_combinationsvariations_seller_custom_fieldvariations_picture_idsvariations_sold_quantityvariations_available_quantityvariations_idvariations_pricesite_idlisting_type_idpriceattributesattributes_value_idattributes_attribute_group_idattributes_nameattributes_value_nameattributes_attribute_group_nameattributes_idbuying_modetags_0tagstags_1listing_sourceparent_item_idcoverage_areascategory_iddescriptions_0descriptionslast_updatedinternational_delivery_modepictures_sizepictures_secure_urlpictures_max_sizepictures_urlpictures_qualitypictures_idpicturesofficial_store_iddifferential_pricingaccepts_mercadopagooriginal_pricecurrency_idthumbnailtitleautomatic_relistdate_createdsecure_thumbnailstop_timestatusvideo_idcatalog_product_idsubtitleinitial_quantitystart_timesold_quantityavailable_quantity0
89990ArgentinaCapital FederalCABASin garantíaNaNsuspendedusedNaNNaN65.00True[][]Falsenot_specifiedNoneNaNEfectivoMLAMOGNaN8326621157NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze65.00NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA5161651339NaNMLA11655{'id': 'MLA7341411094-941461162'}NaN2015-10-12T11:36:14.000Znone500x375https://a248.e.akamai.net/mla-s1-p.mlstatic.com/4053-MLA7341411094_702-O.jpg640x480http://mla-s1-p.mlstatic.com/4053-MLA7341411094_702-O.jpg4053-MLA7341411094_702NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/4053-MLA7341411094_702-I.jpgCuerpo&mente En DeportesFalse2015-10-10T01:36:47.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/4053-MLA7341411094_702-I.jpg1449625007000pausedNoneNaNNaT1144444100700001used
89991ArgentinaCapital Federalcapital<NA>NaNNaNusedNaNNaN250.00True[][]Falseme2NoneNaNEfectivoMLAMOGNaN6547439045NaN[{'value_id': '91997', 'name': 'Color Primario', 'value_name': 'Naranja', 'id': '83000'}, {'value_id': '82071', 'name': 'Talle', 'value_name': '40', 'id': '73002'}]None[20076-MLA20182060030_102014, 20027-MLA20182060035_102014, 20082-MLA20182059647_102014]0.01.09.550015e+09250.0MLAfree250.00NaNSeason-All-SeasonDFLTSeasonAll-SeasonOtrosSeasonbuy_it_nowNaNNaNNaNNoneNaNMLA370638{'id': 'MLA6071406523-920160551'}NaN2015-09-14T16:06:20.000Znone375x500https://a248.e.akamai.net/mla-s2-p.mlstatic.com/20082-MLA6071406523_102014-O.jpg480x640http://mla-s2-p.mlstatic.com/20082-MLA6071406523_102014-O.jpg20082-MLA6071406523_102014NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/20076-MLA6071406523_102014-I.jpgExcelentes Zuecos !!! Imperdibles!!!!False2015-09-14T16:06:16.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/20076-MLA6071406523_102014-I.jpg1447430776000activeNoneNaNNaT1144224677600001used
89992ArgentinaCapital FederalBelgrano<NA>NaNNaNusedNaNNaN1000.00True[][]Falsenot_specifiedNoneNaNEfectivoMLAMOGNaN2024252139NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze1000.00NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA4393729970NaNMLA11456{'id': 'MLA3772436980-913829616'}NaN2015-09-10T20:02:49.000Znone500x375https://a248.e.akamai.net/mla-s1-p.mlstatic.com/117001-MLA3772436980_032015-O.jpg1200x900http://mla-s1-p.mlstatic.com/117001-MLA3772436980_032015-O.jpg117001-MLA3772436980_032015NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/325001-MLA3772436980_032015-I.jpgAcademia Nacional De Bellas Artes 1878 1928 CincuentenarioFalse2015-09-07T13:38:31.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/325001-MLA3772436980_032015-I.jpg1446817111000activeNoneNaNNaT1144163311100001used
89993ArgentinaBuenos AiresFlorida<NA>NaNNaNnewNaNNaN350.00True[][]Falsenot_specifiedNoneNaNEfectivoMLAMOGNaN4094359441NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze350.00NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA4987270443NaNMLA61231{'id': 'MLA5392583991-929806912'}NaN2015-09-25T14:52:40.000Znone500x500https://a248.e.akamai.net/mla-s2-p.mlstatic.com/977101-MLA5392583991_032015-O.jpg500x500http://mla-s2-p.mlstatic.com/977101-MLA5392583991_032015-O.jpg977101-MLA5392583991_032015NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/977101-MLA5392583991_032015-I.jpgBateria 12n5-3b Yamaha Ybr 125 C.c! En Wagner Hermanos!False2015-09-25T14:52:37.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/977101-MLA5392583991_032015-I.jpg1448376757000activeNoneNaNNaT101443192757000010new
89994ArgentinaCórdobaAlmafuerte<NA>NaNNaNnewNaNNaN1200.00False[][]Falseme2NoneNaNNaNNaNNaNNaN1347894917NaN[{'value_id': '92025', 'name': 'Color Primario', 'value_name': 'Negro', 'id': '83000'}, {'value_id': '102000', 'name': 'Talle', 'value_name': 'U', 'id': '93000'}]None[21770-MLA20217510316_122014, 21732-MLA20217511048_122014]0.01.09.349580e+091200.0MLAbronze1200.00NaNSeason-All-SeasonDFLTSeasonAll-SeasonOtrosSeasonbuy_it_nowdragged_bids_and_visitsNaNNaNMLA7093735993NaNMLA373722{'id': 'MLA4648632078-902857580'}NaN2015-08-24T19:55:04.000Znone281x500https://a248.e.akamai.net/mla-s1-p.mlstatic.com/21732-MLA4648632078_122014-O.jpg675x1200http://mla-s1-p.mlstatic.com/21732-MLA4648632078_122014-O.jpg21732-MLA4648632078_122014NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/21770-MLA4648632078_122014-I.jpgMini Vestido/remera De Pailets Super Cool- ImportadoFalse2015-08-24T19:55:02.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/21770-MLA4648632078_122014-I.jpg1445630102000activeNoneNaNNaT1144044610200001new
89995ArgentinaCapital Federalcapital federalSin garantíaNaNNaNusedNaNNaN68.00False[][]Falseme2NoneNaNNaNNaNNaNNaN9451922715NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze68.00NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA1130924824NaNMLA1227{'id': 'MLA1315520302-935540165'}NaN2015-10-02T13:36:51.000Znone84x126https://a248.e.akamai.net/mla-s2-p.mlstatic.com/13612-MLA1315520302_3624-O.jpg84x126http://mla-s2-p.mlstatic.com/13612-MLA1315520302_3624-O.jpg13612-MLA1315520302_3624NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/13612-MLA1315520302_3624-I.jpgEl Fin De Las Libertades - Benegas Lynch (h) - Usado !!False2015-10-02T13:36:50.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/13612-MLA1315520302_3624-I.jpg1448977010000activeNoneNaNNaT1144379301000001used
89996ArgentinaCapital FederalNúñez<NA>NaNNaNnewNaNNaN126.00True[][]FalsecustomNoneNaNEfectivoMLAMOGNaN4665194056NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze126.00NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA1317691731NaNMLA45559{'id': 'MLA6934377054-927516382'}NaN2015-10-05T17:45:04.000Znone500x373https://a248.e.akamai.net/mla-s2-p.mlstatic.com/15283-MLA6934377054_052014-O.jpg1200x896http://mla-s2-p.mlstatic.com/15283-MLA6934377054_052014-O.jpg15283-MLA6934377054_052014NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/15240-MLA6934377054_052014-I.jpgHonda Wave Guardabarro Interior TraseroFalse2015-09-22T23:30:21.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/15240-MLA6934377054_052014-I.jpg1448148621000pausedNoneNaNNaT1144296462100011new
89997ArgentinaBuenos AiresLa Matanza<NA>NaNNaNnewNaNNaN300.00True[][]Falsenot_specifiedNoneNaNEfectivoMLAMOGNaN3046474001NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze300.00NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA5098165723NaNMLA81061{'id': 'MLA5644559490-902956215'}NaN2015-08-24T21:33:54.000Znone500x312https://a248.e.akamai.net/mla-s1-p.mlstatic.com/600001-MLA5644559490_032015-O.jpg1200x750http://mla-s1-p.mlstatic.com/600001-MLA5644559490_032015-O.jpg600001-MLA5644559490_032015NaNNaNNaTTrueNaNARShttp://mla-s1-p.mlstatic.com/600001-MLA5644559490_032015-I.jpgMy Little Pony Completa Latino 4 TemporadasFalse2015-08-24T21:33:51.000Zhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/600001-MLA5644559490_032015-I.jpg1445636031000activeNoneNaNNaT1144045203100001new
89998ArgentinaChubutTrelewLa garantia solo responde en caso de fallas de encuadernacion o impresion en el libro. En ese caso se restituira el libro por uno en condiciones una vez devuelto el fallado, los gastos extra de envio corren por cuenta del compradorNaNNaNnewNaNNaN696.58False[][]Falsenot_specifiedNoneNaNAcordar con el compradorMLAWCGNaN2373910598NaNNaNNaNNaNNaNNaNNaNNaNMLAbronze696.58NaNNaNNaNNaNNaNNaNNaNbuy_it_nowdragged_bids_and_visitsNaNNaNMLA3153148762NaNMLA48851{'id': 'MLA3520244075-935837959'}NaN2015-10-02T19:21:05.000Znone225x225https://a248.e.akamai.net/mla-s2-p.mlstatic.com/4664-MLA3520244075_022013-O.jpg225x225http://mla-s2-p.mlstatic.com/4664-MLA3520244075_022013-O.jpg4664-MLA3520244075_022013NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/4664-MLA3520244075_022013-I.jpgAccidente Cerebrovascular En La Infancia Y AdolescenciaFalse2015-10-02T19:20:56.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/4664-MLA3520244075_022013-I.jpg1448997656000activeNoneNaNNaT10014438136560000100new
89999ArgentinaCapital Federalmataderos<NA>NaNNaNusedNaNNaN470.00True[][]Falsenot_specifiedNoneNaNNaNNaNNaNNaN9518711314NaN[{'value_id': '92004', 'name': 'Color Primario', 'value_name': 'Dorado', 'id': '83000'}, {'value_id': '82058', 'name': 'Color Secundario', 'value_name': 'Negro', 'id': '73001'}, {'value_id': '101995', 'name': 'Talle', 'value_name': 'M', 'id': '93000'}]None[716501-MLA20366662883_082015, 410601-MLA20366670840_082015, 439501-MLA20366675127_082015, 309501-MLA20366673959_082015]0.01.09.786027e+09470.0MLAfree470.00NaNSeason-Autumn-WinterFINDSeasonAutumn-WinterFicha técnicaSeasonbuy_it_nowdragged_bids_and_visitsNaNNaNMLA3661010119NaNMLA109390{'id': 'MLA8241142190-943040617'}NaN2015-10-12T23:54:16.000Znone281x500https://a248.e.akamai.net/mla-s2-p.mlstatic.com/309501-MLA8241142190_082015-O.jpg675x1200http://mla-s2-p.mlstatic.com/309501-MLA8241142190_082015-O.jpg309501-MLA8241142190_082015NaNNaNNaTTrueNaNARShttp://mla-s2-p.mlstatic.com/716501-MLA8241142190_082015-I.jpgCampera De Mujer Tucci ReversibleFalse2015-10-12T23:54:13.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/716501-MLA8241142190_082015-I.jpg1449878053000activeNoneNaNNaT1144469405300001used